SELFATTENTION GENERATIVE ADVERSARIAL NETWORKS


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SELF-ATTENTION GENERATIVE ADVERSARIAL NETWORKS

Self-Attention Generative Adversarial Networks Figure 1. The proposed SAGAN generates images by leveraging complementary features in distant portions of the image rather than local regions of fixed shape to generate consistent objects/scenarios. In each row, the first image shows five representative query locations with color coded dots. Pub on Mon, 03 Feb 2020 07:17:00 GMT
Source: https://arxiv.org/pdf/1805.08318.pdf
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SELF-ATTENTION GENERATIVE ADVERSARIAL NETWORKS

In this work, we propose Self-Attention Generative Adversarial Networks (SAGANs), which in-troduce a self-attention mechanism into convolutional GANs. The self-attention module is com-plementary to convolutions and helps with modeling long range, multi-level dependencies across image regions. Armed with self-attention, the generator can draw ... Pub on Thu, 18 Jul 2019 01:13:00 GMT
Source: https://arxiv.org/pdf/1805.08318v1.pdf
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SELF-ATTENTION GENERATIVE ADVERSARIAL NETWORKS (SAGAN)

Self-Attention Generative Adversarial Networks (SAGAN) (a.k.a. Finally, Here Are Some Dogs with Separated Legs) Overview Introduces self-attention mechanism into convolutional GAN’s For image generation tasks With spectral normalization Achieves state-of-the-art results Pub on Tue, 28 Jan 2020 20:53:00 GMT
Source: https://aisc.ai.science/static/slides/20180611_XiyangChen.pdf
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SELF-ATTENTION GENERATIVE ADVERSARIAL NETWORKS

3 Self-Attention Generative Adversarial Networks Most GAN-based models [22, 26, 10] for image generation are built using convolutional layers. Convolution processes the information in a local neighborhood, thus using convolutional layers alone is computationally inefficient for modeling long-range dependencies in images. In this section, we Pub on Sat, 08 Feb 2020 07:33:00 GMT
Source: http://www.icst.pku.edu.cn/struct/Seminar/WenjingWang_181202/WenjingWang_181202.pdf
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SELF-ATTENTION GENERATIVE ADVERSARIAL NETWORKS

Self-Attention Generative Adversarial Networks Han Zhang12, Ian Goodfellow2, Dimitris Metaxas1, Augustus Odena2 1Rutgers University, 2Google Brain. Which GAN paper are we talking about? What did we do? • Add Self-Attention blocks to Generator and Discriminator • Spectral Normalization (Miyato et al., ICLR, 2018) in both ... Pub on Tue, 04 Feb 2020 16:27:00 GMT
Source: http://www.augustusodena.com/assets/sagan_icml_slides.pdf
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SELF-SUPERVISED ADVERSARIAL HASHING NETWORKS FOR CROSS ...

Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval Chao Li1, Cheng Deng∗1, Ning Li1, Wei Liu∗2, Xinbo Gao1, and Dacheng Tao3 1School of Electronic Engineering, Xidian University, Xi’an 710071, China 2Tencent AI Lab, Shenzhen, China 3UBTECH Sydney AI Centre, SIT, FEIT, University of Sydney, Australia, li chao@stu.xidian.edu.cn, {chdeng.xd, ningli2017}@gmail.com, wliu ... Pub on Sun, 09 Feb 2020 13:08:00 GMT
Source: http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Self-Supervised_Adversarial_Hashing_CVPR_2018_paper.pdf
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GENERATIVE ATTENTION MODEL WITH ADVERSARIAL SELF-LEARNING ...

paper we propose a novel generative attention model obtained by adversarial self-learning. The proposed adversarial attention produces more diverse visual attention maps and it is able to gener-alize the attention better to new questions. The experiments show the proposed adversarial attention leads to a state-of-the-art VQA Pub on Fri, 07 Feb 2020 19:01:00 GMT
Source: https://ilija139.github.io/pub/mm17.pdf
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ATTENTIVE SEMANTIC AND PERCEPTUAL FACES COMPLETION USING ...

We propose an approach based on self-attention generative adversarial networks to accom- ... [14], which trains two adversarial networks simultaneously to capture data distribution of input images. Therefore, a typical GANs net-work consists of a generator and a discriminator, in which the generator tries to learn the ... Pub on Tue, 11 Feb 2020 19:03:00 GMT
Source: http://www.cs.newpaltz.edu/%7Elik/publications/Xiaowei-Liu-NPL-2019.pdf
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ADVERSARIAL SELF-DEFENSE FOR CYCLE-CONSISTENT GANS

lot of attention in recent years. Current state-of-art methods [34, 20, 11, 15, 4, 10] solve this task using generative adversarial networks [8] that usually consist of a pair of generator and discriminator networks that are trained in a min-max fashion to generate realistic images from the target domain Pub on Sun, 16 Feb 2020 07:37:00 GMT
Source: https://papers.nips.cc/paper/8353-adversarial-self-defense-for-cycle-consistent-gans.pdf
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RELGAN: R GENERATIVE ADVERSARIAL NETWORKS FOR T GENERATION

Generative adversarial networks (GANs) have achieved great success at gener-ating realistic images. However, the text generation still remains a challenging task for modern GAN architectures. In this work, we propose RelGAN, a new GAN architecture for text generation, consisting of three main components: a re- Pub on Sun, 16 Feb 2020 13:21:00 GMT
Source: https://openreview.net/pdf?id=rJedV3R5tm
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