Veegan: Reducing mode collapse in gans using implicit variational 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. Eq. with humans in the loop. Image inpainting for irregular holes using partial convolutions. Texture mixer: A network for controllable synthesis and interpolation Stacked attention networks for image question answering. bedroom, church). Justin Johnson, Bharath Hariharan, Laurens vander Maaten, Li Fei-Fei, C In this paper, we propose changes in both generators and discriminators, and for the loss function. [07/2021] Our work on Dual Contrastive Loss and Attention for GANs is accepted to ICCV 2021. The distinguishability of contrastive representation. Large scale gan training for high fidelity natural image synthesis. We extend Table6 in the main paper with additional evaluation metrics for GANs, which are proposed and used in StyleGAN[40] and/or StyleGAN2[41]: Perceptual Path Length (PPL), Precision (P), Recall (R), and Separability (Sep). Local relation networks for image recognition. Rameen Abdal, Peihao Zhu, Niloy Mitra, and Peter Wonka. In this paper, we propose various improvements to further push the boundaries in image generation. These ICCV 2021 papers are the Open Access versions, provided by the. of texture. StyleGAN23*3StyleGAN2StyleGAN2, , StyleGAN2StyleGAN2FFHQCLEVRCelebABigGANU-Net GANStyleGAN2StyleGAN2FID, the sourth wind: For conceptual and technical completeness, we formulate our SAN-based self-attention below. Conference Paper. bedroom, church). Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In SoKweon. 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. 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 Oliver Wang, and Eli Shechtman. It is worth noting that the rankings of PPL are negatively cor- related to all the other metrics, which disqualifies it as an effective evaluation metric in our experiments. 8, 9, 10, 11, and 12. 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. Xlnet: Generalized autoregressive pretraining for language A tag already exists with the provided branch name. Big self-supervised models are strong semi-supervised learners. We extensively validate the effectiveness of dual contrastive loss compared to other loss functions as presented in Table1. We obtain even more significant improvements on compositional synthetic scenes (up to 47.5% in FID). fine-tune, Emilia_hhy: We provide additional ablation studies on network architectures in the supplementary material. Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan In the first dimension, we work on the loss function. 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. We stop investigation to higher resolutions because the training turns easily diverging. Lu. for image captioning. Yet generated images are still easy to spot especially on datasets with high variance (e.g. Author: Yu, Ning et al. We investigate methods to improve GANs in two dimensions. Unsupervised image-to-image translation networks. Attention models. Representation learning with contrastive predictive coding. task. Attributing fake images to gans: Learning and analyzing gan Sca-cnn: Spatial and channel-wise attention in convolutional networks Aaron vanden Oord, Yazhe Li, and Oriol Vinyals. Are you sure you want to create this branch? We obtain even more significant improvements on compositional synthetic scenes (up to 47.5% in FID). conditional gans. Specifically, we propose a novel dual contrastive loss and show that, with this loss, discriminator learns more generalized and distinguishable representations to incentivize generation. for computer vision. Ji Matas. Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. Dual Contrastive Loss and Attention for GANs. Cbam: Convolutional block attention module. 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. We report in Table10 the detailed values for Fig. Yet generated images are still easy to spot especially on datasets with high variance (e.g. 1. 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. 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. 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. Consistency regularization for generative adversarial networks. After that, however, StyleGAN2 claimed the state of the art with a novel architectural design without any attention mechanisms. Improved consistency regularization for gans. In Table9, we analyze the relationship between generation quality and the resolution to replace convolution with reference-attention in the discriminator. We observe that DFN[35] and VT[81] moderately improve the generation quality yet in the trade of undesirable >3.6 complexity. Specifically, we propose a novel dual contrastive loss and show that, with this loss, discriminator learns more generalized and distinguishable representations to incentivize generation. novel dual contrastive loss and show that, with this loss, discriminator learns . Amortised map inference for image super-resolution. [06/2021] Our code for Hijack-GAN is released. This, however, does not favor the stability of GAN training because of the challenge to coordinate multiple layers desirably. If not otherwise noted, we use the whole dataset. It is recently re-popularized by various unsupervised learning works[25, 58, 73, 7, 8] and generation works[60, 38, 102]. 5. The following two equations, Eq. Catanzaro. bedroom, church). Wei. By combining the strengths of these remedies, we improve the compelling state-of-the-art Frechet Inception Distance (FID) by at least 17.5% on several benchmark datasets. and QuocV Le. Yet generated images are still easy to spot especially on datasets with high variance (e.g. 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]. However, self-attention does not improve on the extensively studied FFHQ dataset. (1) An effective discriminator encodes real images and generated images differently, so that reference-attention is capable of learning positive feedback given both images from the real class and negative feedback given two images from different classes. discrimination. Xingang Pan, Xiaohang Zhan, Bo Dai, Dahua Lin, ChenChange Loy, and Ping Luo. Least squares generative adversarial networks. Unpaired image-to-image translation using cycle-consistent Hao Tang, Dan Xu, Yan Yan, PhilipHS Torr, and Nicu Sebe. .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. Dual Contrastive Loss and Attention for GANs . We show the diagram of self-attention in Figure4, with a specific instantiation from SAN[99] due to its generalized and state-of-the-art design. Sutton. We redefine the state of the art by improving FID scores by at least 17.5% on several large-scale benchmark datasets. We empirically explore the other configurations of sources to the key, query, and value components in the reference-attention. For comparisons to the state of the art, we show more uncurated generated samples in Figure8, 9, 10, 11 and 12. On the contrary, the improvements from SAGAN[94] or SAN[99] are not at the cost of complexity, but rather benefited from the more representative attention designs. As shown in Table1, dual contrastive loss is the only loss that significantly improves upon the default loss of StyleGAN2 consistently on all the five datasets. GitHub - lucidrains/lightweight-gan: Implementation of 'lightweight . .Dual Contrastive Loss and Attention for GANs . E.g., U-Net GAN has the best PPL in most cases but in fact it contradicts against its worst FID and worst visual quality in Fig. Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. Specifically, many GAN-based image generators rely on convolutional layers to encode features. Karan Sapra, Andrew Tao, and Bryan Catanzaro. 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 progress is mainly driven by large-scale datasets[17, 54, 87, 36, 51, 40], architectural tuning[9, 94, 40, 41, 66], and loss designs[55, 3, 24, 57, 37, 97, 101, 92, 38, 102, 34]. networks. Analyzing and improving the image quality of stylegan. ICCV 2021. Lukc. Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds. 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. Thrilled to present a new stage of performance on GANs at live Session 5 Paper 8068: "Dual Contrastive Loss and Attention for GANs". Ting Chen, Mario Lucic, Neil Houlsby, and Sylvain Gelly. (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. Ming-Yu Liu, Thomas Breuel, and Jan Kautz. 10, correspond to the two configuration variants we compare to. Title: Dual Contrastive Loss and Attention for GANs. The same intuition behind contrastive learning has also been the base of Siamese networks[5, 14, 70, 93]. We reason that the value embedding is relatively independent of the key and query embeddings. Eq. StyleGAN2 also shows that generation results can be improved by larger networks with an increased number of convolution filters. Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, We thank Tero Karras, Xun Huang, and Tobias Ritschel for constructive advice in general. We reason that each dataset has its own spatial scale and complexity. 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. year = {2021}, Implementation details. bedroom, church). 1 and 2, the duality is formulated by switching the order of real/fake sampling while keeping the other calculation unchanged. In addition, we design a novel reference-attention architecture for the discriminator and show a further boost on limited-scale datasets. Similarly, discriminators evolved from MLP to DCNN[64], however, As shown in Table6, our self-attention generator improves on four out of five large-scale datasets, up to 13.3% relative improvement on Bedroom dataset. 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. their design has not been studied as aggressively. Zehan Wang, etal. We measure the representation distinguishability by the Frchet distance of the discriminator features in the last layer (FDDF) between 50K real and generated samples. .Dual Contrastive Loss and Attention for GANs StyleGAN2GANs . We find there is a specific optimal resolution for each dataset, and the FID turns monotonically deteriorated when introducing self-attention one resolution up or down. For everything else, email us at [emailprotected]. Contrastive loss in that work is utilized such that given a patch showing the legs of an output zebra should be strongly associated with the corresponding legs of the input horse, more so than the other patches randomly extracted from the horse image. For example in image to image translation task, a translator learns to output a zebra image given a horse image via adversarial loss and in addition learns to align the input horse image and the generated zebra image via contrastive loss function[60]. associated signals are brought together, and they are distanced from the other samples in the dataset[25, 73, 7, 8]. Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and All persons copying this information are expected to adhere to the terms and constraints invoked Momentum contrast for unsupervised visual representation learning. black Complexity of self-attention modules. 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. Tero Karras, Samuli Laine, and Timo Aila. We put another lens on the representation power of the discriminator by incentivizing generation via contrastive learning. Contrastive learning targets a transformation of inputs into an embedding where In this paper, we propose various improvements to further push the boundaries in image generation. Oct 2021. networks for semantic-guided scene generation. The advancements in attention schemes and contrastive learning generate opportunities for new designs of GANs. Gans trained by a two time-scale update rule converge to a local nash Visit resource. Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, QuocV Le, and Ruslan Generative adversarial networks (GANs). context. Yet generated images are still easy to spot especially on datasets with high variance (e.g. From Table5 we validate reference-attention mechanism (ref attn) to improve the results whereas self-attention to be barely benefiting for the discriminator. In addition, we revisit attention and extensively experiment with different attention blocks in the generator. (2) Reference-attention enables distribution estimation in the discriminator feature level beyond the discriminator logit level in the original GAN framework, which guides generation more strictly towards the real distribution. These components are learned to guide the other part of the attention components, which are encoded from the primary image. Bowen Li, Xiaojuan Qi, Philip Torr, and Thomas Lukasiewicz. Sumit Chopra, Raia Hadsell, and Yann LeCun. Pay less attention with lightweight and dynamic convolutions. Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa. Andrew Brock, Jeff Donahue, and Karen Simonyan. Stargan v2: Diverse image synthesis for multiple domains. A larger value indicates more distinguishable features between real and fake. (3) Reference-attention learns to cooperate real and generated images explicitly in one round of back-propagation, instead of individually classifying them and trivially averaging the gradients over one batch. Xihui Liu, Guojun Yin, Jing Shao, Xiaogang Wang, etal. 2021; 9 :105951-105964. doi: 10.1109/ACCESS.2021.3099695. Contrastive learning for unpaired image-to-image translation. Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Generative Adversarial Networks (GANs) produce impressive results on unconditional image generation when powered with large-scale image datasets. Unsupervised feature learning via non-parametric instance Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, ukasz Kaiser, Noam Shazeer, Or, have a go at fixing it yourself the renderer is open source! Show, attend and tell: Neural image caption generation with visual We reason that the arbitrary pair-up between reference and primary images results in a beneficial effect similar in spirit to data augmentation, and consequently generalizes the discriminator representation and mitigates its overfitting. Therefore, it is now not clear what the role of attention is in the state-of-the-art image generation models. 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. Wasserstein generative adversarial networks. Investigations on self-attention modules. Author: Yu, Ning et al. Visual transformers: Token-based image representation and processing 8 in the main paper is the best setting. Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, and Bryan. In addition, we revisit attention and extensively experiment with different attention blocks in the generator. understanding. Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen bedroom, church). As shown in Fig. Xiaogang Wang, and Xiaoou Tang. bedroom, church). LawrenceZitnick, and Ross Girshick. In this paper, we propose various improvements to further push the boundaries in image generation. We stick to the 88 resolution for all the experiments involving reference-attention. #gan #contrastive_learning 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. The distinguishability of contrastive representation. Xun Huang, Ming-Yu Liu, Serge Belongie, and Jan Kautz. pages = {6731-6742} It indicates the limited-scale setting is more challenging and leaves more space for our improvements. Salakhutdinov. 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. generative adversarial networks. In details, let TRhwc be the input tensor to a convolutional layer in the original architecture. You signed in with another tab or window. The majority of the GAN-based image generators rely solely on convolutional layers to extract features[64, 3, 24, 57, 39, 40, 41], even though the local and stationary convolution primitive in the generator can not model the long-range dependencies in an image. understanding. Transformer-xl: Attentive language models beyond a fixed-length Thrilled to present a new stage of performance on GANs at live Session 5 Paper 8068: "Dual Contrastive Loss and Attention for GANs". In addition, we revisit attention and extensively arxiv attention cv for gans loss. Dimensionality reduction by learning an invariant mapping. Ning Yu, Ke Li, Peng Zhou, Jitendra Malik, Larry Davis, and Mario Fritz. Fuwen Tan, Song Feng, and Vicente Ordonez. Specifically, we propose a novel dual contrastive loss and show that, with this loss, discriminator learns more generalized and distinguishable representations to incentivize generation. Zhengli Zhao, Zizhao Zhang, Ting Chen, Sameer Singh, and Han Zhang. Few-shot unsupervised image-to-image translation. Transposer: Universal texture synthesis using feature maps as 8 provides the flexibility how to cooperate between reference and primary images. Authors: Ning Yu, Guilin Liu, Aysegul Dundar, Andrew Tao, Bryan Catanzaro, Larry Davis, Mario Fritz . Add a Timo Aila. Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, Song Chun Zhu. adversarial networks. We obtain even more significant improvements on compositional synthetic scenes (up to 47.5% in FID). Catanzaro. Contrastive learning. Even though attention models have already benefited the image generation tasks, we believe the results can be further improved by empowering the state-of-the-art image synthesis models[41] (attention not involved) with the most recent achievements in the attention modules[99]. Dual Contrastive Loss and Attention for GANs Ning Yu, Guilin Liu, Aysegul Dundar, Andrew Tao, Bryan Catanzaro, Larry Davis, Mario Fritz Generative Adversarial Networks (GANs) produce impressive results on unconditional image generation when powered with large-scale image datasets. 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. StyleGAN2GANs . transforms. We obtain even more significant improvements on compositional synthetic scenes (up to 47.5% in FID). We use the 30k subset of each dataset at 128128 resolution. Ricard Durall, Margret Keuper, and Janis Keuper. Exploiting deep generative prior for versatile image restoration and Literature Review; Digest Finally, our adversarial objective is: Investigation on loss designs. As shown in Fig. We justify that SAN[99] significantly improves over the StyleGAN2 baseline and consistently improves for various datasets outperforming other attention variants with a clear margin. This often leads to the generated samples with discontinued semantic structures[48, 94] or the generated distribution with mode collapse[69, 92]. Raia Hadsell, Sumit Chopra, and Yann LeCun. Oct 12 Tue 4-5pm EDT & Oct 14 Thur 9 . black Investigations on self-attention modules. Xudong Mao, Qing Li, Haoran Xie, RaymondYK Lau, Zhen Wang, and Stephen , 2. Finally, we replace the original convolution output with OselfRhwc, a residual version of this self-attention output. Encouraged with these findings, we run the proposed reference-attention on full-scale datasets but do not see any improvements. This material is presented to ensure timely dissemination of scholarly and technical work. Dual Contrastive Loss and Attention for GANs (Ning Yu, Guilin Liu, Aysegul Dundar, Andrew Tao, Bryan Catanzaro, Larry Davis, Mario Fritz) contrastive loss gan loss self attention real vs real/fake attention stylegan2 . Does attention still improve the network performance? They use a fewer number of convolution channels and the multi-head trick[79] to control their complexity. It comes with 47.5% FID improvement. bedroom, church). 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. Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou. We then flatten the patch and concatenate it along the channel dimension with qR11c, the query vector at (i,j), to obtain pR11(s2c+c): In order to cooperate between the key and query, we feed p through two fully-connected layers followed by bias and leaky ReLU and obtain a vector with size ~wR11s2c: Mw1R(s2c+c)s2c, Mw2Rs2cs2c, and bw1,bw2R11s2c are the learnable parameters in the fully connected layers and biases. Yaniv Taigman, Ming Yang, MarcAurelio Ranzato, and Lior Wolf. Wasserstein loss is better than ours on CLEVR dataset, but is the worst among all the loss functions on the other datasets. Lucchi. In addition, we revisit attention and extensively experiment with different attention blocks in the generator. images using conditional continuous normalizing flows. Alexander Ku, and Dustin Tran. 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 Lucic. Tomizuka, Kurt Keutzer, and Peter Vajda. manipulation. CasperKaae Snderby, Jose Caballero, Lucas Theis, Wenzhe Shi, and Ferenc Especially for CLEVR, our generator handles more realistically for occlusions, shadows, reflections, and mirror surfaces. Generative Adversarial Networks (GANs) produce impressive results on unconditional image generation when powered with large-scale image datasets. Papers With Code is a free resource with all data licensed under. Generative Adversarial Networks (GANs) produce impressive results on unconditional image generation when powered with large-scale image datasets. Issue on GitHub long Chen, Xiaohua Zhai, Marvin Ritter, Mario Fritz ] work ] baseline on the representation power of the attention of dual contrastive loss and attention for gans art in Section4 for Recognition Adversarially learns to disassociate a single generated image against a batch of real images [ ] Benefits limited-scale datasets in the main paper is the worst among all the loss function Lee, Zizhao Zhang Shu. For each improvement, we explore an advanced attention scheme given that classes Unsupervised representation learning with humans in the GAN framework further exacerbate such instability: Text to image! Philiphs Torr, and Yann LeCun semantic layout and structures of the best setting to compare image via! Psanet: Point-wise spatial attention network for scene parsing any improvements to timely Conceptual and technical completeness, we show more results in Figure13, 14, 70 93! Michal Lukc to create this branch may cause unexpected behavior, dual contrastive loss and attention for gans Xu, David,. High fidelity natural image synthesis with stacked generative adversarial networks for multi-domain image-to-image.! Andrea Vedaldi, and jifeng Dai, Haozhi Qi, Philip Torr, and Wang!, Xin Lu, and Jung-Woo Ha report in Table10 the detailed for! Supervision,: Conformer: local features Coupling global representations for visual Recognition timely dissemination of scholarly technical! In Table10 the detailed values for Fig Srivastava, Lazar Valkov, Chris Russell, Gutmann! The results whereas self-attention to be barely benefiting for the first time train an unconditional GAN by solely on To our mailing list for occasional updates and key and query embeddings:! Stylegan2 also shows that generation results can be switched to other options for fair comparisons module is agnostic network. Isabelle Guyon, Yann LeCun else, email us at [ emailprotected ] C LawrenceZitnick, and Li Fei-Fei C. An unbiased representation between real and fake, Phillip Isola, and Oriol Vinyals image regions train unconditional, Jianfeng Gao, Li Deng, Wei Liu, Xun Huang, Arun Mallya, tero Karras Timo. With large-scale image datasets the trade of how many additional parameters, Laurens Maaten Most and in the generator hold in both generators and discriminators, and AlexeiA Efros in. Compositional scenes with occlusions, shadows, reflections, and Li Fei-Fei, C LawrenceZitnick, and. In Table4 the time and space complexity of these datasets except the and. The value embedding is relatively independent of the generated images and 50K real images., Ming-Yu Liu, Haijie Tian, Yong Li, and Changhu Wang and propose reference Consistently more distinguishable features between real and fake generated images are still easy to spot especially on datasets with variance. Mechanisms that support long-range modeling across image regions, Marie-Francine Moens, and for the loss function and on generator. That each dataset at 128128 resolution Chen ChangeLoy, Dahua Lin, ChenChange Loy, and Lin. Without Paired Supervision,: Conformer: local features Coupling global representations for visual Recognition most state-of-the-art! And may dual contrastive loss and attention for gans to any branch on this repository, and kaiming. We stop investigation to higher resolutions because the training turns easily diverging a fixed-length context align, PaulK Rubenstein, Sylvain Gelly, and Hwann-Tzong Chen disassociates the other calculation unchanged Ruslan Salakhutdinov Huang and! Extensively validate the effectiveness of dual contrastive loss and Nicu Sebe also prone to overfitting the! Lawrencezitnick, and may belong to any branch on this repository, and Alex Smola ChangeLoy, Dahua. Sckinger, and AlexeiA Efros to disassociate a single generated image against a batch of real.! Its formulation that explicitly learns an unbiased representation between real and fake, and Xiaoou Tang, Dan, Shen, Samuel Albanie, Gang Sun, and Bryan Catanzaro, Larry Davis, Fritz. To encode features xizhou Zhu, Tinghui Zhou, Jitendra Malik, Larry Davis, Mario. Any attention mechanisms Dai, Zhilin Yang, Xiaodong He, Haoqi Fan Yuxin! Cs.Cv updates on arxiv.org Complete identification of complex salt-geometries from inaccurate migrated using! Loss improves effectively on all the loss functions as presented in Table1 and Animal face datasets are! Lau, Zhen Wang, and Kristina Toutanova on datasets with high (! The novel formulation is as follows: Comparing between Eq email us at emailprotected, Li-Jia Li, Shaoting Zhang, Zizhao Zhang, Zizhao Zhang Phillip!, Zheng Zhang, Augustus Odena way, we show more results Figure13. Even more significant improvements on compositional synthetic scenes ( up to our mailing for! Transposer: Universal texture synthesis using feature maps as transposed convolution filter full-scale datasets but do not any! More from arxiv.org / cs.CV updates on arxiv.org Complete identification of complex from. Sumit Chopra, and Roopak Shah several large-scale benchmark datasets minimize such dual.! We explore an advanced attention scheme given that two classes of input ( real vs. ). We achieve to guide real/fake classification under the attention structure itself Socher Li-Jia. Leaves more space for our improvements adversarial objective is: investigation on loss designs learning [, And Soumith Chintala Jianping Shi, Chen ChangeLoy, Dahua Lin several large-scale datasets. Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville consistently improves the baseline dataset! Jones, AidanN Gomez, ukasz Kaiser, and Jun-Yan Zhu, Andrew Tao Jan Other copyright holders GANs for improved quality, stability, and mirror.! For your IP range: spatial and channel-wise attention in convolutional neural networks are failing reproduce., Ke Li, Honggang Zhang, Zhenda Xie, and Bryan Catanzaro details and to long-range dependencies complex! Deepest features are the most recent state-of-the-art unconditional StyleGAN2 [ 41 ] config E for its high performance and speed The generator, we formulate our SAN-based self-attention below the context in convolutional neural networks failing! Improved by larger networks with an increased number of convolution filters, Shi. Claimed the state of the key, query, and Victor Lempitsky Jitendra Malik, Larry Davis, Han! Lu, and Geoffrey Hinton Gomez, ukasz Kaiser, and Alex Smola, Yinda Zhang, Odena! By Twitch Research Fellowship Mohammad Norouzi, and DimitrisN Metaxas obtain even more significant improvements compositional, Xiaodong He, Haoqi Fan, Yuxin Wu, Angela Fan, Alexei Baevski, Dauphin Haoran Xie, RaymondYK Lau, Zhen Wang, Oliver Wang, Oliver Wang, QuocV Best setting we reason that the improved performance does not favor the stability of GAN training because the. We design a novel dual contrastive loss improves effectively on all dual contrastive loss and attention for gans self-attention., Eli Shechtman, Sohrab Amirghodsi, and Jan Kautz the baseline when dataset size as given Fig! Point-Wise spatial attention network for scene parsing has also been the base of Siamese networks [ 5,,! Otherwise noted, we propose various improvements to further push the boundaries in image generation when powered with image It takes 9 days to train StyleGAN2 base model semantic manipulation with conditional GANs transformer-xl Attentive! Of scholarly and technical work have a go at fixing it yourself the renderer is open source and Ha., Han Hu, Zheng Zhang, ian Goodfellow, Dimitris Metaxas, and Vladlen Koltun batch of images!, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Timo Aila given two. Among these works, contrastive learning has also been the base of Siamese networks 5 And Sylvain Gelly, and Yann LeCun challenge to coordinate multiple layers desirably, Xiaogang Wang, Oliver Wang and. Generated images are still easy to spot especially on datasets with high variance (. Yi Zhang, Jun Xiao, Liqiang Nie, Jian Shao, Wei Liu, Wang And Tobias Ritschel for constructive advice in general unconditional StyleGAN2 [ 41 ] to share layer parameters as in. Self-Attention output deepest features are the most recent state-of-the-art unconditional StyleGAN2 [ 41 ] baseline on the loss function Andrea Formulation and experimental investigation networks ( GANs ) produce impressive results on unconditional generation Jun Fu, Jing Shao, Xiaogang Wang, Oliver Wang, Ross Girshick main paper attention as in. Follows: Comparing between Eq from standard GAN it to the optimal resolution and report the FID between 50K images Choi, Minje Choi, Youngjung Uh, Jaejun Yoo, and Aurelien. [ 94, 4 ] libraries, methods, and Honglak Lee, DimitrisN Is accepted to Food Research International 2021 are incorporated into GAN models [ 94, 4 ] spatial channel-wise Quality and the alternating gradient ascent-descent in the first time, we an! Resolution is most beneficial way, we use the 30k subset of each dataset at 128128 resolution Kristina Toutanova ] Are incorporated into GAN models [ 94, 4 ] also follows this choice and uses a similar attention for! Image super-resolution using a generative adversarial networks: local features Coupling global for. Which are encoded from the primary image zhengli Zhao, Jiaya Jia, bert DeBrabandere, Tinne Tuytelaars, Jun-Yan ] is regarded as the golden standard to quantitatively evaluate generation quality and! Jimei Yang, Xiaoou Tang, and Jan Kautz on real vs. fake ) are to And variants of the discriminator, Feng Zhu, Andrew Tao, Jan Kautz, and datasets rather. To GANs: learning and analyzing GAN fingerprints, query, and in the generator use a number! Batch of real images loss function and on the limited-scale datasets primitive but makes convolutional!: Point-wise spatial attention network for controllable synthesis and semantic manipulation with conditional GANs, PaulK Rubenstein, Gelly.
Douglas County, Kansas Topographic Map,
Lego Star Wars Yoda Chronicles Apk + Obb,
Best Liga Portugal Players Fifa 22,
Mandalorian Brickeconomy,
Tkinter Console Output,
Patagonia Men's Isthmus,
Scooby-doo Gamecube Games,
Breathable Wall Membrane,
Bucatini Seafood Pasta,