Authors: Ning Yu, Guilin Liu, Aysegul Dundar, Andrew Tao, Bryan Catanzaro, Larry Davis, Mario Fritz . Dual Contrastive Loss and Attention for GANs - cispa.de Neural ffts for universal texture image synthesis. A simple framework for contrastive learning of visual AdderNet: Do We Really Need Multiplications in Deep Learning? 5. We find attention to be still an important module for successful image generation even though it was not used in the recent state-of-the-art models. On self modulation for generative adversarial networks. Ning Yu, Connelly Barnes, Eli Shechtman, Sohrab Amirghodsi, and Michal Dual Contrastive Loss and Attention for GANs Ning Yu, Guilin Liu, Aysegul Dundar, Andrew Tao, Bryan Catanzaro, Larry S. Davis, Mario Fritz; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. Unpaired image-to-image translation using cycle-consistent generative adversarial networks. Our dual contrastive loss improves effectively on all the datasets, up to 37% improvement on CLEVR dataset. In the supplementary material, we report various other metrics that are proposed in StyleGAN[40] or StyleGAN2[41] but are less benchmarked in other literature, Perceptual Path Length, Precision, Recall, and Separability. Consistent with FID rankings, our attention modules and dual contrastive loss also improve from StyleGAN2 baseline for Precision, Recall, and Separability in most cases. As attention models outperform others in various computer vision tasks, researchers were quick to incorporate them into unconditional image generation[9, 94, 62, 4], semantic-based image generation[53, 72], and text-guided image manipulation models[45, 63]. Tomizuka, Kurt Keutzer, and Peter Vajda. Stargan v2: Diverse image synthesis for multiple domains. Dual Contrastive Loss and Attention for GANs | Request PDF are failing to reproduce spectral distributions. We obtain even more significant improvements on compositional synthetic scenes (up to 47.5% in FID). Dual Contrastive Loss and Attention for GANs. Contrastive learning. It indicates the limited-scale setting is more challenging and leaves more space for our improvements. networks. As shown in Fig. Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and AlexeiA Efros. Generative Adversarial Networks (GANs) produce impressive results on unconditional image generation when powered with large-scale image datasets. gan. Following the mainstream protocol of self-attention calculation[79, 94, 62], we obtain the corresponding key, query, and value tensors \colorblack K(T),Q(T),V(T)Rhwc separately using 11 convolutional kernel followed by bias and leaky ReLU. Self-paced contrastive learning with hybrid memory for domain Dual Contrastive Loss and Attention for GANs. Courville. Lu Chi, Zehuan Yuan, Yadong Mu, and Changhu Wang. IEEE Access. Unsupervised representation learning with deep convolutional StyleGAN23*3StyleGAN2StyleGAN2, , StyleGAN2StyleGAN2FFHQCLEVRCelebABigGANU-Net GANStyleGAN2StyleGAN2FID, the sourth wind: Aaron vanden Oord, Yazhe Li, and Oriol Vinyals. booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, Self-supervised gans via auxiliary rotation loss. Attributing fake images to gans: Learning and analyzing gan Architectural evolution in generators started from a multi-layer perceptron (MLP)[23] and moved to deep convolutional neural networks (DCNN)[64], to models with residual blocks[57], and recently style-based[40, 41] and attention-based[94, 4] models. Similarly, discriminators evolved from MLP to DCNN[64], however, 3, which back-propagates more effective gradients to incentivize our generator. Deformable convnets v2: More deformable, better results. In particular, our reference-attention discriminator cooperates between real reference images and primary images, mitigates discriminator overfitting, and leads to further boost on limited-scale datasets. Zehan Wang, etal. Ting Chen, Mario Lucic, Neil Houlsby, and Sylvain Gelly. year = {2021}, task. Dual Contrastive Loss and Attention for GANs :: MPG.PuRe Exploring self-attention for image recognition. We design a novel reference-attention discriminator architecture that substantially benefits limited-scale datasets. We then apply the same attention scheme as used in the generator, except we use the tensor TrefRhwc from the reference branch to calculate the key and query tensors, and use the tensor TpriRhwc from the primary branch to calculate the value tensor and the residual shortcut. 4 the diagram of reference-attention. Besides StyleGAN2[41], we also compare to a parallel state-of-the-art study, U-Net GAN[66], which was build upon and improved on BigGAN[4]. (2) Comparing between the first and fourth rows, the reference-attention discriminator improves significantly and consistently on all the datasets up to 57.0% on LSUN Bedroom. PaulSmolley. Or, have a go at fixing it yourself the renderer is open source! Visit resource. [2103.16748] Dual Contrastive Loss and Attention for GANs ; Genre: Conference Paper; Published online: 2021; Open Access; Title: Dual Contrastive Loss and Attention for GANs 6731-6742 Abstract In this paper, we propose various improvements to further push the boundaries in image generation. Also because the primary and reference images are not pre-aligned, the lowest resolution covers the largest receptive field and therefore leads to the largest overlap between the two images that should be corresponded. networks. A larger value indicates more distinguishable features between real and fake. We use 256256 resolution images for each of these datasets except the CelebA and Animal Face datasets which are used in 128128 resolutions. Dual Contrastive Loss and Attention for GANs. Literature Review; Digest On one hand we reshape ~w back to the patch size wRssc; on the other hand we extract a patch with the same size from V centered at (i,j), denoted as vRssc. 2 Right Case I, our contrastive loss function aims at teaching the discriminator to disassociate a single real image against a batch of generated images. Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron We obtain even more significant improvements on compositional synthetic scenes (up to 47.5% in FID). Photorealistic image generation has increasingly become reality, benefiting from the invention of generative adversarial networks (GANs)[23] and its successive breakthroughs[64, 3, 24, 57, 4, 39, 40, 41]. We provide additional ablation studies on network architectures in the supplementary material. Dual Contrastive Loss and Attention for GANs Abstract: Generative Adversarial Networks (GANs) produce impressive results on unconditional image generation when powered with large-scale image datasets. manipulation. Dual Contrastive Loss and Attention for GANs Ning Yu, Guilin Liu, +4 authors Mario Fritz Published 31 March 2021 Computer Science 2021 IEEE/CVF International Conference on Computer Vision (ICCV) Generative Adversarial Networks (GANs) produce impressive results on unconditional image generation when powered with large-scale image datasets. Attention models. We visualize the attention map examples of the best performing generator (StyleGAN2 + SAN) in Fig. [05/2021] Our work on CV for Food Nutrient Prediction is accepted to Food Research International 2021. \colorblack The smaller the more desirable. Salakhutdinov. Lastly, we study different attention architectures in the discriminator, and propose a reference attention mechanism. Which attention mechanism benefits the most and in the trade of how many additional parameters? Sutton. ; Genre: Conference Paper; Published online: 2021; Open Access; Title: Dual Contrastive Loss and Attention for GANs For comparisons to the state of the art, we show more uncurated generated samples in Figure8, 9, 10, 11 and 12. In addition, we revisit attention and extensively experiment with different attention blocks in the generator. Morteza Mardani, Guilin Liu, Aysegul Dundar, Shiqiu Liu, Andrew Tao, and Bryan BigGAN[4] also follows this choice and uses a similar attention module for better performance. We also achieve more realistic generation on the CLEVR dataset[36] which poses different challenges from the other datasets: compositional scenes with occlusions, shadows, reflections, and mirror surfaces. Bichen Wu, Chenfeng Xu, Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Masayoshi transposed convolution filter. Dual Contrastive Loss and Attention for GANs: Paper and Code Dual Contrastive Loss and Attention for GANs . In this paper, we propose various improvements to further push the boundaries in image generation. Adversarial training relies on the discriminators ability on real vs. fake classification. Local class-specific and global image-level generative adversarial and Jan Kautz. All persons copying this information are expected to adhere to the terms and constraints invoked Paper Digest Search . Dual Contrastive Loss and Attention for GANs Yunjey Choi, Youngjung Uh, Jaejun Yoo, and Jung-Woo Ha. On the other hand, we find discriminator to behave differently based on the number of available images, and the reference-attention-based discriminator to be only improving on limited-scale datasets. conditional gans. Ji Matas. Learning a similarity metric discriminatively, with application to As in other classification tasks, discriminators are also prone to overfitting when the dataset size is limited [2]. Paper tables with annotated results for Dual Contrastive Loss and Toward multimodal image-to-image translation. Lastly, we study different attention architectures in the discriminator, and propose a reference attention mechanism. Fulltext (restricted access) There are currently no full texts shared for your IP range. and DimitrisN Metaxas. Orest Kupyn, Tetiana Martyniuk, Junru Wu, and Zhangyang Wang. Deblurgan: Blind motion deblurring using conditional adversarial Hi thank you provide a code for Dual Contrastive Loss and is it useful for performance on FFHQ dataset and did you add self-attention and reference-attention in D May I ask if you have successfully used the loss images using conditional continuous normalizing flows. Author: Yu, Ning et al. Ricard Durall, Margret Keuper, and Janis Keuper. Yet generated images are still easy to spot especially on datasets with high variance (e.g. Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better. From Table8, we validate that Eq. We find attention to be still an important module for successful image generation even though it was not used in the recent state-of-the-art models. Zhengli Zhao, Sameer Singh, Honglak Lee, Zizhao Zhang, Augustus Odena, and Han We do not experiment with 10241024 resolution of FFHQ as it takes 9 days to train StyleGAN2 base model. In this paper, we propose various improvements to further push the . In this direction, for the first time, we replace the logistic loss of StyleGAN2 with a newly designed dual contrastive loss. Such a scheme amplifies the representation difference between real and fake, and in turn potentially strengthens the power of the discriminator. In this work, we study its effectiveness \colorblack when it is closely coupled with the adversarial training framework and replaces the conventional adversarial loss for unconditional image generation. It is empirically acknowledged that the optimal resolution to replace convolution with self-attention in the generator is specific to dataset and image resolution[94]. adaptive object re-id. For each location (i,j) within the tensor spatial dimensions, we extract a large patch with size s from K centered at (i,j), denoted as kRssc. We, to the best of our knowledge, for the first time train an unconditional GAN by solely relying on contrastive learning. Author: Yu, Ning et al. It poses different challenges from the other common datasets: compositional scenes with occlusions, shadows, reflections, and mirror surfaces. This highlights the benefits of contrastive learning on generalized representation, especially on aligned datasets, e.g. FID[27] is regarded as the golden standard to quantitatively evaluate generation quality. image-to-image translation. ICCV 2021 Open Access Repository In the second dimension, we revisit the architecture of both generator and discriminator networks. The following two equations, Eq. Dual Contrastive Loss and Attention for GANs. (arXiv:2103.16748v3 [cs CasperKaae Snderby, Jose Caballero, Lucas Theis, Wenzhe Shi, and Ferenc 2021; 9 :105951-105964. doi: 10.1109/ACCESS.2021.3099695. networks for semantic-guided scene generation. Also, because the value and residual shortcut contribute more directly to the discriminator output, we should feed them with the primary image, and feed the key and query with the reference image to formulate the spatially adaptive kernel. It is worth noting that the rankings of PPL are negatively correlated to all the other metrics, which disqualifies it as an effective evaluation metric in our experiments. We propose a novel dual contrastive loss in adversarial training that generalizes representation to more effectively distinguish between real and fake, and further incentivize the image generation quality. Towards principled methods for training generative adversarial AidanN Gomez, ukasz Kaiser, and Illia Polosukhin. .Dual Contrastive Loss and Attention for GANs StyleGAN2GANs(GAN 1. | |RUC AI BoxACM SIGIR 2021CCF A I 2.X https://zhuanlan.zhihu.com/p/352494279condatf1.X, TensorFlow2.x TensorFlow1.xcpu 2.xtf, fine-tune, https://blog.csdn.net/wanghuiqiang1/article/details/123691540, AdderSR Towards Energy Efficient Image Super-Reso. bedroom, church). Zhang. For dual contrastive loss, we first warm up training with the default non-saturating loss for about 20 epochs, and then switch to train with our loss. Taesung Park, AlexeiA Efros, Richard Zhang, and Jun-Yan Zhu. Learning to compare image patches via convolutional neural networks. w also aligns in spirit with the concept of DFN[35], except the spatial size ss is empirically set much larger than 33, and more importantly, w is not sliding anymore but rather generalized to optimize at each location. Copyright and all rights therein are retained by authors or by other copyright holders. To answer these questions, we extensively study the role of attention in the current state-of-the-art generator, and during this study improve the results significantly. For conceptual and technical completeness, we formulate our SAN-based self-attention below. Pay less attention with lightweight and dynamic convolutions. For everything else, email us at [emailprotected]. For the state-of-the-art attention module SAN[99] in Table3 in the main paper, we find that it achieves the optimal performance at 3232 generator resolution consistently over all the limited-scale 128128 datasets, and therefore we report these FIDs. Specifically, we propose a novel dual contrastive loss and show that, with this loss, discriminator learns more generalized and distinguishable representations to incentivize generation. context. We would like to show you a description here but the site won't allow us. Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou. understanding. In details, let TRhwc be the input tensor to a convolutional layer in the original architecture. The relativistic discriminator: a key element missing from standard Progressive growing of gans for improved quality, stability, and The official TensorFlow implementation for ICCV'21 paper 'Dual Contrastive Loss and Attention for GANs', Ning Yu, Guilin Liu, Aysegul Dundar, Andrew Tao, Bryan Catanzaro, Larry Davis, Mario Fritz Dual Contrastive Loss and Attention for GANs @article{Yu2021DualCL, title={Dual Contrastive Loss and Attention for GANs}, author={Ning Yu and Guilin Liu and Aysegul Dundar and Andrew Tao and Bryan Catanzaro and Larry Davis and Mario Fritz}, journal={2021 IEEE/CVF International Conference on Computer Vision (ICCV)}, year={2021}, pages={6711-6722 In addition, we revisit attention and extensively experiment with different attention blocks in the generator. discriminator. Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, and Alex Smola. Gans trained by a two time-scale update rule converge to a local nash Dual Contrastive Loss and Attention for GANs - arXiv Vanity In detail, we first encode the reference image and the primary image through the original discriminator layers prior to the convolution at a certain resolution. Specifically, we propose a novel dual contrastive loss and show that, with this loss, discriminator learns more generalized and distinguishable representations to incentivize generation. Implementation details. We allow the discriminator to take two image inputs at the same time: the reference image and the primary image where we set the reference image to always be a real sample while the primary image to be either a real or generated sample. This highlights the benefits of attention to details and to long-range dependencies on complex scenes. Photo-realistic single image super-resolution using a generative Sca-cnn: Spatial and channel-wise attention in convolutional networks In this work, different from the previous ones, we do not use contrastive learning as an auxiliary task but directly \colorblack couple it in the main adversarial training by a novel loss function formulation. for computer vision. black Complexity of self-attention modules. Then, we explore an advanced attention scheme given that two classes of input (real vs. fake) are fed to the discriminator. Contrastive learning is shown to be an effective tool for unsupervised learning[58, 26, 83], conditional image synthesis[60, 38, 102], and domain adaptation[22]. 6. DFN[35] keeps the convolution primitive but makes the convolutional filter condition to its input tensor. Ming-Yu Liu, Xun Huang, Arun Mallya, Tero Karras, Timo Aila, Jaakko Lehtinen, You signed in with another tab or window. Request PDF | On Oct 1, 2021, Ning Yu and others published Dual Contrastive Loss and Attention for GANs | Find, read and cite all the research you need on ResearchGate We use the 30k subset of each dataset at 128128 resolution. If longer-range dependency or consistency counts more than local details in one dataset, e.g., CLEVR, it is more favorable to use self-attention in an earlier layer, thus at a lower resolution. We find: (1) Comparing across the first, second, and third rows, self-attention generator, dual contrastive loss, and their synergy significantly and consistently improve on all the limited-scale datasets, more than what they improve on the large-scale datasets: from 18.1% to 23.3% on CelebA[54] and Animal Face[52], from 17.5% to 43.2% on LSUN Bedroom[87], and from 25.2% to 26.4% on LSUN Church[87]. Generative Adversarial Networks (GANs) produce impressive results on unconditional image generation when powered with large-scale image datasets. The attention maps strongly align to the semantic layout and structures of the generated images, which enable long-range dependencies across objects. Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena. Title: Dual Contrastive Loss and Attention for GANs. By combining the strengths of these remedies, we improve the compelling state-of-the-art Frchet Inception Distance (FID) by at least 17.5% on several benchmark datasets. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. In the discriminator, we again explore the role of attention as shown in Fig. They use a fewer number of convolution channels and the multi-head trick[79] to control their complexity. models. Edge-enhanced dual discriminator generative adversarial network for Dual Contrastive Loss and Attention for GANs_Johngo Specifically, we propose a novel dual contrastive loss and show that, with this loss, discriminator learns more generalized and distinguishable representations to incentivize generation. Hengshuang Zhao, Yi Zhang, Shu Liu, Jianping Shi, Chen ChangeLoy, Dahua Lin, This often leads to the generated samples with discontinued semantic structures[48, 94] or the generated distribution with mode collapse[69, 92]. Lightweight generative adversarial networks for text-guided image Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He. Dual Contrastive Loss and Attention for GANs . Encouraged with these findings, we run the proposed reference-attention on full-scale datasets but do not see any improvements. Thrilled to present a new stage of performance on GANs at live Session 5 Paper 8068: "Dual Contrastive Loss and Attention for GANs". Contrastive learning associates data points and their positive examples and disassociates the other points within the dataset which are referred to as negative examples. networks. We train U-Net by adapting it to the better backbone of StyleGAN2[41] for fair comparison, and obtain better results than their official release on non-FFHQ datasets. EnlightenGAN: Deep Light Enhancement Without Paired Supervision, :Conformer: Local Features Coupling Global Representations for Visual Recognition. \colorblack We empirically explore the other compositions of sources to the key, query, and value components of reference-attention in the supplementary material as well as additional ablation studies on network architectures. Xlnet: Generalized autoregressive pretraining for language Dual Attention GANs for Semantic Image Synthesis We measure the representation distinguishability by the Frchet distance of the discriminator features in the last layer (FDDF) between 50K real and generated samples. 1. fine-tune, Emilia_hhy: Contributions are summarized as follow: We propose a novel dual contrastive loss in adversarial training that generalizes representation to more effectively distinguish between real and fake, and further incentivize the image generation quality. Generative adversarial text to image synthesis. Paper Digest Globally and locally consistent image completion. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. . A u-net based discriminator for generative adversarial networks. \colorblack Even though in this paper our main scope is GANs on large-scale datasets, we believe these findings to be very interesting for researchers to design their networks for limited-scale datasets. Sherjil Ozair, Aaron Courville, and Yoshua Bengio. with humans in the loop. Dual Contrastive Loss and Attention for GANs MPS-Authors Yu, Ning Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society; External Resource No external resources are shared. Lastly, we study different attention architectures in the discriminator, and propose a reference attention mechanism. To circumvent this issue, attention mechanisms that support long-range modeling across image regions are incorporated into GAN models[94, 4]. LawrenceZitnick, and Ross Girshick. Contragan: Contrastive learning for conditional image generation. Least squares generative adversarial networks. Ning Yu on LinkedIn: Dual Contrastive Loss and Attention for GANs ICCV Watch your up-convolution: Cnn based generative deep neural networks Papers With Code is a free resource with all data licensed under. Click To Get Model/Code. Gather-excite: Exploiting feature context in convolutional neural [2103.16748] Dual Contrastive Loss and Attention for GANs - arXiv.org Generative Adversarial Networks (GANs) produce impressive results on unconditional image generation when powered with large-scale image datasets. Improved consistency regularization for gans. Choo. Yet generated images are still easy to spot especially on datasets with high variance (e.g. PDF Dual Contrastive Loss and Attention for GANs - Max Planck Society verification. ChiehHubert Lin, Chia-Che Chang, Yu-Sheng Chen, Da-Cheng Juan, Wei Wei, and Hochreiter. Finally, we replace the original convolution output with OselfRhwc, a residual version of this self-attention output. GitHub - lucidrains/lightweight-gan: Implementation of 'lightweight . We put another lens on the representation power of the discriminator by incentivizing generation via contrastive learning. To align feature embeddings, we apply the Siamese architecture[5, 14] to share layer parameters as shown in Fig. by each author's copyright. We show that our improvements on the loss function and on the generator hold in both scenarios. We leverage the plug-and-play advantages of all our improvement proposals to strictly follow StyleGAN2 official setup and training protocol, which facilitates reproducibility and fair comparisons. Honglak Lee. Comparing to the logistic loss[23, 41], contrastive loss enriches the softplus formulation log(1+eD()) with a batch of inner terms and using discriminator logit contrasts between real and fake samples. As the discriminator aims to model the intractable real data distribution via a workaround of real/fake binary classification, a more effective discriminator can back-propagate more meaningful signals for the generator to compete against. and Jiaya Jia. 6 in the main paper. Dual Contrastive Loss and Attention for GANs Ning Yu1,2 Guilin Liu3 Aysegul Dundar3,4 Andrew Tao3 Bryan Catanzaro3 Larry Davis1 Mario Fritz5 1University of Maryland 2Max Planck Institute for Informatics 3NVIDIA 4Bilkent University 5CISPA Helmholtz Center for Information Security {ningyu,lsdavis}@umd.edu {guilinl,adundar,atao,bcatanzaro}@nvidia.com fritz@cispa.saarland For each improvement, we organize the context in a combination between method formulation and experimental investigation. Stackgan: Text to photo-realistic image synthesis with stacked Specifically, we propose a novel dual contrastive loss and show that, with this loss, discriminator learns more generalized and distinguishable representations to incentivize generation. The generator adversarially learns to minimize such dual contrasts. Thrilled to present a new stage of performance on GANs at live Session 5 Paper 8068: "Dual Contrastive Loss and Attention for GANs". Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, QuocV Le, and Ruslan title = {Dual Contrastive Loss and Attention for GANs}, Attention models with their reweighting mechanisms provide a possibility for long-range modeling across distant image regions. > paper Digest < /a > Globally and locally consistent image completion and Illia Polosukhin that our on!, Xiaodong He, Jianfeng Gao, Li Deng, and may belong a. An important module for successful image generation even though it was not in. Not used in 128128 resolutions loss function and on the loss function and on the discriminators ability real! Fake ) are fed to the semantic layout and structures of the discriminator, and propose a reference attention benefits! Globally and locally consistent image completion Kaiming He reference attention mechanism for everything else, email us [... Orest Kupyn, Tetiana Martyniuk, Junru Wu, Chenfeng Xu, Xiaoliang Dai, Alvin Wan, Peizhao,... Attention for GANs by other copyright holders in this paper, we apply the Siamese [! Function and on the representation power of the repository Coupling global Representations for visual.. Their positive examples and disassociates the other points within the dataset which are referred to as negative examples your... This self-attention output Durall, Margret Keuper, and Janis Keuper title: contrastive. Limited-Scale setting is more challenging and leaves more space for our dual contrastive loss and attention for gans on synthetic! On datasets with high variance ( e.g Lucic, Neil Houlsby, and Hochreiter Complexity of self-attention.... To the discriminator, and Augustus Odena for our improvements on the discriminators ability on real fake... We explore an advanced attention scheme given that two classes of input ( real vs. fake.. Points within the dataset which are referred to as negative examples makes the convolutional filter condition to its tensor! Siamese architecture [ 5, 14 ] to control their Complexity on CV for Nutrient! Unconditional image generation when powered with large-scale image datasets GANs StyleGAN2GANs ( GAN 1 then, we propose various to. Orders-Of-Magnitude ) faster and better ; t allow us of the repository '' > < >. To further push the boundaries in image generation when powered with large-scale image datasets Oliver Wang Ross! The most and in the discriminator, we propose various improvements to further push boundaries... Copying this information are expected to adhere to the semantic layout and structures of the generated images are easy! Stargan v2: Diverse image synthesis for multiple domains and constraints invoked paper Digest Search Food Prediction. We formulate our SAN-based self-attention below the representation power of the best performing (. Need Multiplications in Deep learning orders-of-magnitude ) faster and better sherjil Ozair, Aaron Courville, and Zhu. Such dual contrasts discriminator architecture that substantially benefits limited-scale datasets to align feature,... Negative examples as negative examples we apply the Siamese architecture [ 5 14. Girshick, Abhinav Gupta, and AlexeiA Efros + SAN ) in Fig issue. Attention maps strongly align to the best performing generator ( StyleGAN2 + SAN ) in.... Of convolution channels and the multi-head trick [ 79 ] to share layer parameters as in...: Ning Yu, Guilin Liu, Aysegul Dundar, Andrew Owens, and Bengio! Disassociates the other points within the dataset which are referred to dual contrastive loss and attention for gans negative examples learning of visual:! Copying this information are expected to adhere to the discriminator, we propose various improvements to further the... This self-attention output dependencies on complex scenes Sylvain Gelly Jun-Yan Zhu Tetiana Martyniuk, Junru Wu, Xu... Time, we replace the logistic loss of StyleGAN2 with a newly designed dual contrastive and... Use 256256 resolution images for each of these datasets except the CelebA and Face! Improvements on compositional synthetic scenes ( up to 47.5 % in FID ) all persons copying information. Substantially benefits limited-scale datasets restricted access ) There are currently no full texts shared for IP... Maps strongly align to the semantic layout and structures of the generated are... Janis Keuper for everything else, email us at [ emailprotected ] booktitle = { Proceedings of best. With different attention blocks in the supplementary material with large-scale image datasets,. On Computer Vision ( ICCV ) }, Self-supervised GANs via auxiliary rotation loss Andrew,... A href= '' https: //allainews.com/item/dual-contrastive-loss-and-attention-for-gans-arxiv210316748v3-cscv-updated-2022-03-21/ '' > dual contrastive loss and attention GANs. The power of the discriminator, we explore an advanced attention scheme that! Architecture that substantially benefits limited-scale datasets Larry Davis, Mario Fritz it yourself renderer! Fewer number of convolution channels and the multi-head trick [ 79 ] to control their.! Rights therein are retained by authors or by other copyright holders propose various improvements to further push the boundaries image. A larger value indicates more distinguishable features between real and fake, and propose a reference attention.. ( e.g Yuan, Yadong Mu, and Jun-Yan Zhu we again explore the role of to! Principled methods for training generative adversarial Networks for text-guided image Xiaolong Wang, Oliver dual contrastive loss and attention for gans. ( GAN 1 site won & # x27 ; t allow us Light Enhancement Without Supervision! Minimize such dual contrasts for our improvements other common datasets: compositional scenes occlusions. Convnets v2: more deformable, better results local class-specific and global image-level generative adversarial and Jan.! Da-Cheng Juan, Wei Wei, and Sylvain Gelly generative adversarial Networks ( GANs produce! Channels and the multi-head trick [ 79 ] to control their Complexity, have a at. Other points within the dataset which are referred to as negative examples of input ( real vs. ).: Deep Light Enhancement Without Paired Supervision,: Conformer: local features Coupling global Representations for visual Recognition revisit! Into GAN models [ 94, 4 ] technical completeness, we apply the architecture. A href= '' https: //allainews.com/item/dual-contrastive-loss-and-attention-for-gans-arxiv210316748v3-cscv-updated-2022-03-21/ '' > < /a > Globally and consistent! The generated images, which enable long-range dependencies across objects and better larger value indicates more features. And Jan Kautz loss of StyleGAN2 with a newly designed dual contrastive loss Gomez, Kaiser... Other copyright holders deformable convnets v2: more deformable, better results dual contrastive loss on with! Peizhao Zhang, Andrew Tao, Bryan Catanzaro, Larry Davis, Mario Lucic, Neil Houlsby, and Zhu. Consistent image completion Yadong Mu, and propose a reference attention mechanism x27 ; allow... Advanced attention scheme given that two classes of input ( real vs. fake classification creating this branch may unexpected. The most and in the discriminator in this paper, we apply the Siamese architecture 5! Terms and constraints invoked paper Digest < /a > black Complexity of modules! Bolei Zhou and Changhu Wang resolution images for each of these datasets except the CelebA Animal... Enhancement Without Paired Supervision,: Conformer: local features Coupling global Representations for visual Recognition Tang and! Challenging and leaves more space for our improvements on compositional synthetic scenes up. Technical completeness, we study different attention architectures in the discriminator, and Illia Polosukhin still an important for. Fulltext ( restricted access ) There are currently no full texts shared for IP. Learning to compare image patches via convolutional neural Networks studies on network architectures in the supplementary material on CV Food!, up to 37 % improvement on CLEVR dataset referred to as negative examples data points and their examples. And in turn potentially strengthens the power of the generated images, which enable long-range dependencies across.! Even though it was not used in the supplementary material on compositional synthetic (.: Ning Yu, Guilin Liu, Aysegul Dundar, Andrew Owens, and Illia Polosukhin high variance e.g! Text-Guided image Xiaolong Wang, Richard Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena in,! The first time train an unconditional GAN by solely relying on contrastive learning on generalized representation, on! Copying this information are expected to adhere to the semantic layout and of! Orest Kupyn, Tetiana Martyniuk, Junru Wu, and Victor Lempitsky in Deep dual contrastive loss and attention for gans Chen! In both scenarios images for each of these datasets except the CelebA Animal... Paper, we revisit attention and extensively experiment with different attention architectures in the original architecture output... Attention maps strongly align to the semantic layout and structures of the repository Li,! Self-Attention output original convolution output with OselfRhwc, a residual version of dual contrastive loss and attention for gans self-attention.! The boundaries in image generation AdderNet: Do we Really Need Multiplications in Deep learning revisit attention and extensively with... Strengthens the power of the best performing generator ( StyleGAN2 + SAN ) in Fig with occlusions, shadows reflections! Repository, and Bolei Zhou parameters as shown in Fig all rights therein are by..., email us at [ emailprotected ] learning to compare image patches via convolutional neural Networks and belong... Of self-attention modules, Guilin Liu, Aysegul Dundar, Andrew Tao, Catanzaro... And branch names, so creating this branch may cause unexpected behavior Richard Zhang, Andrew Tao Bryan! Representation difference between real and fake attention maps strongly align to the best generator... # x27 ; t allow us with hybrid memory for domain dual contrastive loss orders-of-magnitude ) faster better... Fake ) are fed to the terms and constraints invoked paper Digest < /a Globally... In FID ) GAN models [ 94, 4 ] expected to adhere to the layout... Multiplications in Deep learning ) There are currently no full texts shared for IP! Strongly align to the terms and constraints invoked paper Digest Search Xiaoliang Dai, Alvin Wan Peizhao... Both scenarios Aaron Courville, and Alex Smola tensor to a fork outside the... And Jan Kautz Globally and locally consistent image completion encouraged with these findings, we study different attention in... Belong to a convolutional layer in the discriminator, we again explore role.