/R42 86 0 R T* >> In this paper, we propose a principled GAN framework for full-resolution image compression and use it to realize 1221. an extreme image compression system, targeting bitrates below 0.1bpp. /R50 108 0 R T* /CA 1 [ (In) -287.00800 (spite) -288.00800 (of) -287.00800 (the) -287.00400 (great) -287.01100 (progress) -288.01600 (for) -287.01100 (GANs) -286.99600 (in) -287.00100 (image) -288.01600 (gener) 19.99670 (\055) ] TJ We propose Graphical Generative Adversarial Networks (Graphical-GAN) to model structured data. T* << 4.02187 -3.68711 Td /R10 11.95520 Tf 14 0 obj /R144 201 0 R /Type /Page >> [ (4) -0.30019 ] TJ /R52 111 0 R /MediaBox [ 0 0 612 792 ] f Generative Adversarial Networks, or GANs for short, were first described in the 2014 paper by Ian Goodfellow, et al. What is a Generative Adversarial Network? ArXiv 2014. << [ (Xudong) -250.01200 (Mao) ] TJ endobj Abstract: The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. >> [ (e) 25.01110 (v) 14.98280 (en) -281.01100 (been) -279.99100 (applied) -280.99100 (to) -281 (man) 14.99010 (y) -279.98800 (real\055w) 9.99343 (orld) -280.99800 (tasks\054) -288.00800 (such) -281 (as) -281.00900 (image) ] TJ A major recent breakthrough in classical machine learning is the notion of generative adversarial … Inspired by recent successes in deep learning we propose a novel approach to anomaly detection using generative adversarial networks. 0 g In this paper, we propose Car-toonGAN, a generative adversarial network (GAN) frame-work for cartoon stylization. /Type /Catalog /ExtGState << [ (W) 79.98660 (e) -327.00900 (ar) 17.98960 (gue) -327 (that) -326.99000 (this) -327.01900 (loss) -327.01900 (function\054) -345.99100 (ho) 24.98600 (we) 25.01540 (v) 14.98280 (er) 39.98350 (\054) -346.99600 (will) -327.01900 (lead) -327 (to) -326.99400 (the) ] TJ /F2 190 0 R [ (xudonmao\100gmail\056com\054) -599.99200 (itqli\100cityu\056edu\056hk\054) -599.99200 (hrxie2\100gmail\056com) ] TJ /Rotate 0 >> >> -83.92770 -24.73980 Td /Title (Least Squares Generative Adversarial Networks) 63.42190 4.33906 Td [ (still) -321.01000 (f) 9.99588 (ar) -319.99300 (from) -320.99500 (the) -320.99800 (real) -321.01000 (data) -319.98100 (and) -321 (we) -321.00500 (w) 10.00320 (ant) -320.99500 (to) -320.01500 (pull) -320.98100 (them) -320.98600 (close) ] TJ [ (\037) -0.69964 ] TJ q /R10 10.16190 Tf /R8 55 0 R Awesome paper list with code about generative adversarial nets. /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] /Type /XObject [ (vided) -205.00700 (for) -204.98700 (the) -203.99700 (learning) -205.00700 (processes\056) -294.99500 (Compared) -204.99500 (with) -205.00300 (supervised) ] TJ /Rotate 0 /ExtGState << [ (r) 37.01960 (e) 39.98900 (gular) -399.00300 (GANs\056) -758.98200 (W) 91.98590 (e) -398.99700 (also) -399.00800 (conduct) -399.99300 (two) -399.00600 (comparison) -400.00700 (e) 19.99180 (xperi\055) ] TJ [ (Center) -249.98800 (for) -250.01700 (Optical) -249.98500 (Imagery) -250 (Analysis) -249.98300 (and) -250.01700 (Learning\054) -250.01200 (Northwestern) -250.01400 (Polytechnical) -250.01400 (Uni) 25.01490 (v) 15.00120 (ersity) ] TJ The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. /F2 215 0 R 34.34730 -38.45700 Td << Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adversarial learning idea. T* >> /R50 108 0 R [ (of) -292.01700 (LSGANs) -291.98400 (o) 10.00320 (ver) -291.99300 (r) 37.01960 (e) 39.98840 (gular) -290.98200 (GANs\056) -436.01700 (F) 45.01580 (ir) 10.01180 (st\054) -302.01200 (LSGANs) -291.98300 (ar) 36.98650 (e) -291.99500 (able) -292.01700 (to) ] TJ titled “Generative Adversarial Networks.” Since then, GANs have seen a lot of attention given that they are perhaps one of the most effective techniques for generating large, high-quality … /Font << Several recent work on speech synthesis have employed generative adversarial networks (GANs) to produce raw waveforms. stream /Resources << /R8 11.95520 Tf >> >> [Generative Adversarial Networks, Ian J. Goodfellow et al., NIPS 2016]에 대한 리뷰 영상입니다. /R18 59 0 R In this paper, we address the challenge posed by a subtask of voice profiling - reconstructing someone's face from their voice. [ (1) -0.30019 ] TJ [ (Recently) 64.99410 (\054) -430.98400 (Generati) 24.98110 (v) 14.98280 (e) -394.99800 (adv) 14.98280 (ersarial) -396.01200 (netw) 10.00810 (orks) -395.01700 (\050GANs\051) -394.98300 (\1336\135) ] TJ >> Authors: Kundan Kumar, Rithesh Kumar, Thibault de Boissiere, Lucas Gestin, Wei Zhen Teoh, Jose Sotelo, Alexandre de Brebisson, Yoshua Bengio, Aaron Courville. Abstract

Consider learning a policy from example expert behavior, without interaction with the expert or access to a reinforcement signal. Q Generative adversarial networks (GANs) are a set of deep neural network models used to produce synthetic data. endobj � 0�� Theoretically, we prove that a differentially private learning algorithm used for training the GAN does not overfit to a certain degree, i.e., the generalization gap can be bounded. >> /Type /XObject /R7 32 0 R /XObject << >> 11.95510 TL /F2 183 0 R >> [ (ously) -268.00400 (trai) 0.98758 (n) -267.99000 (a) -268 (discriminator) -267.00400 (and) -267.99000 (a) -267.01900 (generator\072) -344.99100 (the) -267.98500 (discrimina\055) ] TJ ET /x6 17 0 R Generative Adversarial Networks, or GANs for short, were first described in the 2014 paper by Ian Goodfellow, et al. 3 0 obj To bridge the gaps, we conduct so far the most comprehensive experimental study that investigates apply-ing GAN to relational data synthesis. /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] /Type /Page /R97 165 0 R /R18 59 0 R [ (to) -283 (the) -283.00400 (real) -283.01700 (data\056) -408.98600 (Based) -282.99700 (on) -283.00200 (this) -282.98700 (observ) 24.99090 (ation\054) -292.00500 (we) -283.01200 (propose) -282.99200 (the) ] TJ -94.82890 -11.95510 Td >> However, the hallucinated details are often accompanied with unpleasant artifacts. /x24 21 0 R /R126 193 0 R x�l�K��8�,8?��DK�s9mav�d �{�f-8�*2�Y@�H�� ��>ח����������������k��}�y��}��u���f�`v)_s��}1�z#�*��G�w���_gX� �������j���o�w��\����o�'1c|�Z^���G����a��������y��?IT���|���y~L�.��[ �{�Ȟ�b\���3������-�3]_������'X�\�竵�0�{��+��_۾o��Y-w��j�+� B���;)��Aa�����=�/������ /R139 213 0 R >> /R16 51 0 R T* Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio. BT In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Quantum machine learning is expected to be one of the first potential general-purpose applications of near-term quantum devices. q download the GitHub extension for Visual Studio, http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf, [A Mathematical Introduction to Generative Adversarial Nets (GAN)]. Given a sample under consideration, our method is based on searching for a good representation of that sample in the latent space of the generator; if such a representation is not found, the sample is deemed anomalous. We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a … T* /R40 90 0 R The code allows the users to reproduce and extend the results reported in the study. T* [ (mation) -281.01900 (and) -279.98800 (can) -281.01400 (be) -279.99200 (trained) -280.99700 (end\055to\055end) -280.99700 (through) -280.00200 (the) -281.00200 (dif) 24.98600 (feren\055) ] TJ The paper and supplementary can be found here. q Q generative adversarial networks (GANs) (Goodfellow et al., 2014). /R8 55 0 R [ (ha) 19.99670 (v) 14.98280 (e) -359.98400 (sho) 24.99340 (wn) -360.01100 (that) -360.00400 (GANs) -360.00400 (can) -359.98400 (play) -360.00400 (a) -361.00300 (si) 0.99493 <676e690263616e74> -361.00300 (role) -360.01300 (in) -360.00900 (v) 24.98110 (ar) 19.98690 (\055) ] TJ /ProcSet [ /Text /ImageC /ImageB /PDF /ImageI ] -15.24300 -11.85590 Td 11 0 obj [ (Least) -250 (Squar) 17.99800 (es) -250.01200 (Generati) 9.99625 (v) 9.99625 (e) -250 (Adv) 10.00140 (ersarial) -250.01200 (Netw) 9.99285 (orks) ] TJ 11.95590 TL

In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection. We use 3D fully convolutional networks to form the … /Annots [ ] /R87 155 0 R /R79 123 0 R The results show that … /F2 134 0 R 11.95510 TL /R95 158 0 R Generative Adversarial Nets. /Filter /FlateDecode [ (minimizing) -411.99300 (the) -410.98300 (objective) -411.99500 (function) -410.99300 (of) -411.99700 (LSGAN) -410.99000 (yields) -411.99300 (mini\055) ] TJ /R14 48 0 R /R12 44 0 R T* >> T* /ca 1 /R7 32 0 R >> We achieve state-of-the-art … /Type /Page [ (decision) -339.01400 (boundary) 64.99160 (\054) -360.99600 (b) 20.00160 (ut) -338.01000 (are) -339.01200 (still) -339.00700 (f) 9.99343 (ar) -337.99300 (from) -338.99200 (the) -338.99200 (real) -339.00700 (data\056) -576.01700 (As) ] TJ /R12 6.77458 Tf /R137 211 0 R /R12 7.97010 Tf >> /R8 55 0 R [ (genta\051) -277.00800 (to) -277 (update) -278.01700 (the) -277.00500 (generator) -277.00800 (by) -277.00300 (making) -278.00300 (the) -277.00300 (discriminator) ] TJ /ExtGState << >> >> /x15 18 0 R [ (raylau\100cityu\056edu\056hk\054) -600.00400 (zhenwang0\100gmail\056com\054) -600.00400 (steve\100codehatch\056com) ] TJ /Resources << /x12 20 0 R 10 0 0 10 0 0 cm [ (problem) -304.98100 (of) -303.98600 (v) 24.98110 (anishing) -305.01000 (gradients) -304.00300 (when) -304.99800 (updating) -303.99300 (the) -304.99800 (genera\055) ] TJ 48.40600 786.42200 515.18800 -52.69900 re /R8 55 0 R 5 0 obj Training on various image datasets, we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in … /R116 187 0 R [ (Department) -249.99300 (of) -250.01200 (Information) -250 (Systems\054) -250.01400 (City) -250.01400 (Uni) 25.01490 (v) 15.00120 (ersity) -250.00500 (of) -250.01200 (Hong) -250.00500 (K) 35 (ong) ] TJ /x10 23 0 R [ (stability) -249.98900 (of) -249.98500 (LSGANs\056) ] TJ q framework based on generative adversarial networks (GANs). Don't forget to have a look at the supplementary as well (the Tensorflow FIDs can be found there (Table S1)). Instead of the widely used normal distribution assumption, the prior dis- tribution of latent representation in our DBGAN is estimat-ed in a structure-aware way, which … Unlike the CNN-based methods, FV-GAN learns from the joint distribution of finger vein images and … We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. [ (mizing) -327.99100 (the) -328.01600 (P) 79.99030 (ear) 10.00570 (son) ] TJ /R32 71 0 R Although such methods improve the sampling efficiency and memory usage, their sample quality has not yet reached that of autoregressive and flow-based generative models. /F1 184 0 R /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] /Font << Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. /Annots [ ] /R36 67 0 R /Resources << T* 1 1 1 rg << 12 0 obj T* What is a Generative Adversarial Network? /MediaBox [ 0 0 612 792 ] >> /Resources 19 0 R [ (ments) -280.99500 (between) -280.99500 (LSGANs) -281.98600 (and) -280.99700 (r) 37.01960 (e) 39.98840 (gular) -280.98400 (GANs) -280.98500 (to) -282.01900 (ill) 1.00228 (ustr) 15.00240 (ate) -281.98500 (the) ] TJ [ (moid) -328.98400 (cr) 45.01390 (oss) -330.00600 (entr) 44.98640 (opy) -328.99800 (loss) -329.99900 (function\056) -547.98700 (Howe) 14.99500 (ver) 110.99900 (\054) -350.01800 (we) -328.99400 (found) -329.99600 (that) ] TJ /s7 36 0 R 16 0 obj /Resources << /ProcSet [ /Text /ImageC /ImageB /PDF /ImageI ] /S /Transparency q T* /R18 59 0 R 19.67620 -4.33789 Td << /F2 89 0 R >> T* /Contents 122 0 R In this work, we propose a method to generate synthetic abnormal MRI images with brain tumors by training a generative adversarial network using two publicly available data sets of brain MRI. T* << endobj /R42 86 0 R q /R58 98 0 R /R7 32 0 R >> /Parent 1 0 R [ (lem\054) -390.00500 (we) -362.00900 (pr) 44.98390 (opose) -362 (in) -360.98600 (this) -361.99200 (paper) -362 (the) -362.01100 (Least) -361.98900 (Squar) 37.00120 (es) -362.01600 (Gener) 14.98280 (a\055) ] TJ To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss … /XObject << 15 0 obj /R50 108 0 R Please cite the above paper … endobj Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. CartoonGAN: Generative Adversarial Networks for Photo Cartoonization CVPR 2018 • Yang Chen • Yu-Kun Lai • Yong-Jin Liu In this paper, we propose a solution to transforming photos of real-world scenes into cartoon style images, which is valuable and challenging in computer vision and computer graphics. T* /Annots [ ] >> /R37 82 0 R q /R10 39 0 R /R93 152 0 R /R7 gs /ca 1 /R12 7.97010 Tf /F2 9 Tf /Kids [ 3 0 R 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R ] >> >> T* /R151 205 0 R >> /R10 39 0 R 11.95590 TL >> First, LSGANs are able to /S /Transparency >> /R54 102 0 R /R12 7.97010 Tf -11.95510 -11.95470 Td In this paper, we present GANMEX, a novel approach applying Generative Adversarial Networks (GAN) by incorporating the to-be-explained classifier as part of the adversarial networks. For example, a generative adversarial network trained on photographs of human … A type of deep neural network known as the generative adversarial networks (GAN) is a subset of deep learning models that produce entirely new images using training data sets using two of its components.. /R40 90 0 R T* T* T* /Resources << /BBox [ 133 751 479 772 ] 6.23398 3.61602 Td /XObject << /Font << /Group << Please cite this paper if you use the code in this repository as part of a published research project. 11.95590 TL /Annots [ ] Q Our method takes unpaired photos and cartoon images for training, which is easy to use. A generative adversarial network, or GAN, is a deep neural network framework which is able to learn from a set of training data and generate new data with the same characteristics as the training data. /Rotate 0 /R56 105 0 R Inspired by Wang et al. T* << For more information, see our Privacy Statement. endobj /R7 32 0 R [ (1) -0.30091 ] TJ T* 11.95510 TL [ (tiable) -336.00500 (netw) 10.00810 (orks\056) -568.00800 (The) -334.99800 (basic) -336.01300 (idea) -336.01700 (of) -335.98300 (GANs) -336.00800 (is) -336.00800 (to) -336.01300 (simultane\055) ] TJ T* 59.76840 -8.16758 Td %PDF-1.3 T* Majority of papers are related to Image Translation. 55.14880 4.33789 Td /R8 14.34620 Tf Awesome papers about Generative Adversarial Networks. [ <636c6173736902636174696f6e> -630.00400 (\1337\135\054) -331.98300 (object) -314.99000 (detection) -629.98900 (\13327\135) -315.98400 (and) -315.00100 (se) 15.01960 (gmentation) ] TJ /F1 139 0 R /R148 208 0 R 38.35510 TL endstream /R16 51 0 R T* However, these algorithms are not compared under the same framework and thus it is hard for practitioners to understand GAN’s bene ts and limitations. >> 80.85700 0 Td endobj [ (ha) 19.99670 (v) 14.98280 (e) -496 (demonstrated) -497.01800 (impressi) 25.01050 (v) 14.98280 (e) -496 (performance) -495.99600 (for) -497.01500 (unsuper) 20.01630 (\055) ] TJ [ (Raymond) -249.98700 (Y) 129 (\056K\056) -250 (Lau) ] TJ /Length 28 � 0�� stream >> We show that minimizing the objective function of LSGAN yields mini-mizing the Pearson χ2 divergence. /R10 39 0 R /R12 7.97010 Tf >> 4.02227 -3.68828 Td T* Don't forget to have a look at the supplementary as well (the Tensorflow FIDs can be found there (Table S1)). /s5 33 0 R 105.25300 4.33789 Td ��b�];�1�����5Y��y�R� {7QL.��\:Rv��/x�9�l�+�L��7�h%1!�}��i/�A��I(���kz"U��&,YO�! /R10 10.16190 Tf However, the hallucinated details are often accompanied with unpleasant artifacts. >> >> 1 0 0 1 297 35 Tm /ca 1 /R125 194 0 R /Resources << 13 0 obj [ (resolution) -499.99500 (\13316\135\054) -249.99300 (and) -249.99300 (semi\055supervised) -249.99300 (learning) -500.01500 (\13329\135\056) ] TJ BT Learn more. /R20 6.97380 Tf >> 270 32 72 14 re << /R60 115 0 R T* /ExtGState << 4.02305 -3.68750 Td [ (diver) 36.98400 (g) 10.00320 (ence) 15.00850 (\056) -543.98500 (Ther) 36.99630 (e) -327.98900 (ar) 36.98650 (e) -327.98900 (two) -328 <62656e65027473> ] TJ The code allows the users to reproduce and extend the results reported in the study. /R7 32 0 R /Font << /R146 216 0 R /R10 39 0 R [ (functions) -335.99100 (or) -335 (inference\054) -357.00400 (GANs) -336.00800 (do) -336.01300 (not) -334.98300 (require) -335.98300 (an) 15.01710 (y) -336.01700 (approxi\055) ] TJ [ (squar) 37.00120 (es) -348.01900 (loss) -347.01600 (function) -347.98400 (for) -346.98300 (the) -348.01300 (discriminator) 110.98900 (\056) -602.99500 (W) 91.98710 (e) -347.00600 (show) -347.99100 (that) ] TJ T* >> Learn more. >> [ (the) -261.98800 (e) 19.99240 (xperimental) -262.00300 (r) 37.01960 (esults) -262.00800 (show) -262.00500 (that) -262.01000 (the) -261.98800 (ima) 10.01300 (g) 10.00320 (es) -261.99300 (g) 10.00320 (ener) 15.01960 (ated) -261.98300 (by) ] TJ [ (hypothesize) -367.00300 (the) -366.99000 (discriminator) -367.01100 (as) -366.98700 (a) -366.99300 <636c61737369026572> -367.00200 (with) -367.00500 (the) -366.99000 (sig\055) ] TJ >> stream T* T* If nothing happens, download Xcode and try again. /Rotate 0 /R114 188 0 R endobj Please help contribute this list by contacting [Me][zhang163220@gmail.com] or add pull request, ✔️ [UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION], ✔️ [Image-to-image translation using conditional adversarial nets], ✔️ [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks], ✔️ [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks], ✔️ [CoGAN: Coupled Generative Adversarial Networks], ✔️ [Unsupervised Image-to-Image Translation with Generative Adversarial Networks], ✔️ [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation], ✔️ [Unsupervised Image-to-Image Translation Networks], ✔️ [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs], ✔️ [XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings], ✔️ [UNIT: UNsupervised Image-to-image Translation Networks], ✔️ [Toward Multimodal Image-to-Image Translation], ✔️ [Multimodal Unsupervised Image-to-Image Translation], ✔️ [Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation], ✔️ [Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation], ✔️ [Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation], ✔️ [StarGAN v2: Diverse Image Synthesis for Multiple Domains], ✔️ [Structural-analogy from a Single Image Pair], ✔️ [High-Resolution Daytime Translation Without Domain Labels], ✔️ [Rethinking the Truly Unsupervised Image-to-Image Translation], ✔️ [Diverse Image Generation via Self-Conditioned GANs], ✔️ [Contrastive Learning for Unpaired Image-to-Image Translation], ✔️ [Autoencoding beyond pixels using a learned similarity metric], ✔️ [Coupled Generative Adversarial Networks], ✔️ [Invertible Conditional GANs for image editing], ✔️ [Learning Residual Images for Face Attribute Manipulation], ✔️ [Neural Photo Editing with Introspective Adversarial Networks], ✔️ [Neural Face Editing with Intrinsic Image Disentangling], ✔️ [GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data ], ✔️ [Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis], ✔️ [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation], ✔️ [Arbitrary Facial Attribute Editing: Only Change What You Want], ✔️ [ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes], ✔️ [Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation], ✔️ [GANimation: Anatomically-aware Facial Animation from a Single Image], ✔️ [Geometry Guided Adversarial Facial Expression Synthesis], ✔️ [STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing], ✔️ [3d guided fine-grained face manipulation] [Paper](CVPR 2019), ✔️ [SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color], ✔️ [A Survey of Deep Facial Attribute Analysis], ✔️ [PA-GAN: Progressive Attention Generative Adversarial Network for Facial Attribute Editing], ✔️ [SSCGAN: Facial Attribute Editing via StyleSkip Connections], ✔️ [CAFE-GAN: Arbitrary Face Attribute Editingwith Complementary Attention Feature], ✔️ [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks], ✔️ [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks], ✔️ [Generative Adversarial Text to Image Synthesis], ✔️ [Improved Techniques for Training GANs], ✔️ [Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space], ✔️ [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks], ✔️ [Improved Training of Wasserstein GANs], ✔️ [Boundary Equibilibrium Generative Adversarial Networks], ✔️ [Progressive Growing of GANs for Improved Quality, Stability, and Variation], ✔️ [ Self-Attention Generative Adversarial Networks ], ✔️ [Large Scale GAN Training for High Fidelity Natural Image Synthesis], ✔️ [A Style-Based Generator Architecture for Generative Adversarial Networks], ✔️ [Analyzing and Improving the Image Quality of StyleGAN], ✔️ [SinGAN: Learning a Generative Model from a Single Natural Image], ✔️ [Real or Not Real, that is the Question], ✔️ [Training End-to-end Single Image Generators without GANs], ✔️ [DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation], ✔️ [Photo-Realistic Monocular Gaze Redirection Using Generative Adversarial Networks], ✔️ [GazeCorrection:Self-Guided Eye Manipulation in the wild using Self-Supervised Generative Adversarial Networks], ✔️ [MGGR: MultiModal-Guided Gaze Redirection with Coarse-to-Fine Learning], ✔️ [Dual In-painting Model for Unsupervised Gaze Correction and Animation in the Wild], ✔️ [AutoGAN: Neural Architecture Search for Generative Adversarial Networks], ✔️ [Animating arbitrary objects via deep motion transfer], ✔️ [First Order Motion Model for Image Animation], ✔️ [Energy-based generative adversarial network], ✔️ [Mode Regularized Generative Adversarial Networks], ✔️ [Improving Generative Adversarial Networks with Denoising Feature Matching], ✔️ [Towards Principled Methods for Training Generative Adversarial Networks], ✔️ [Unrolled Generative Adversarial Networks], ✔️ [Least Squares Generative Adversarial Networks], ✔️ [Generalization and Equilibrium in Generative Adversarial Nets], ✔️ [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium], ✔️ [Spectral Normalization for Generative Adversarial Networks], ✔️ [Which Training Methods for GANs do actually Converge], ✔️ [Self-Supervised Generative Adversarial Networks], ✔️ [Semantic Image Inpainting with Perceptual and Contextual Losses], ✔️ [Context Encoders: Feature Learning by Inpainting], ✔️ [Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks], ✔️ [Globally and Locally Consistent Image Completion], ✔️ [High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis], ✔️ [Eye In-Painting with Exemplar Generative Adversarial Networks], ✔️ [Generative Image Inpainting with Contextual Attention], ✔️ [Free-Form Image Inpainting with Gated Convolution], ✔️ [EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning], ✔️ [a layer-based sequential framework for scene generation with gans], ✔️ [Adversarial Training Methods for Semi-Supervised Text Classification], ✔️ [Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks], ✔️ [Semi-Supervised QA with Generative Domain-Adaptive Nets], ✔️ [Good Semi-supervised Learning that Requires a Bad GAN], ✔️ [AdaGAN: Boosting Generative Models], ✔️ [GP-GAN: Towards Realistic High-Resolution Image Blending], ✔️ [Joint Discriminative and Generative Learning for Person Re-identification], ✔️ [Pose-Normalized Image Generation for Person Re-identification], ✔️ [Image super-resolution through deep learning], ✔️ [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network], ✔️ [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks], ✔️ [Robust LSTM-Autoencoders for Face De-Occlusion in the Wild], ✔️ [Adversarial Deep Structural Networks for Mammographic Mass Segmentation], ✔️ [Semantic Segmentation using Adversarial Networks], ✔️ [Perceptual generative adversarial networks for small object detection], ✔️ [A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection], ✔️ [Style aggregated network for facial landmark detection], ✔️ [Conditional Generative Adversarial Nets], ✔️ [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets], ✔️ [Conditional Image Synthesis With Auxiliary Classifier GANs], ✔️ [Deep multi-scale video prediction beyond mean square error], ✔️ [Generating Videos with Scene Dynamics], ✔️ [MoCoGAN: Decomposing Motion and Content for Video Generation], ✔️ [ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removal], ✔️ [BeautyGAN: Instance-level Facial Makeup Transfer with Deep Generative Adversarial Network], ✔️ [Connecting Generative Adversarial Networks and Actor-Critic Methods], ✔️ [C-RNN-GAN: Continuous recurrent neural networks with adversarial training], ✔️ [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient], ✔️ [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery], ✔️ [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling], ✔️ [Transformation-Grounded Image Generation Network for Novel 3D View Synthesis], ✔️ [MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions], ✔️ [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks], ✔️ [Boundary-Seeking Generative Adversarial Networks], ✔️ [GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution], ✔️ [Generative OpenMax for Multi-Class Open Set Classification], ✔️ [Controllable Invariance through Adversarial Feature Learning], ✔️ [Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro], ✔️ [Learning from Simulated and Unsupervised Images through Adversarial Training], ✔️ [GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification], ✔️ [1] http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf (NIPS Goodfellow Slides)[Chinese Trans][details], ✔️ [3] [ICCV 2017 Tutorial About GANS], ✔️ [3] [A Mathematical Introduction to Generative Adversarial Nets (GAN)].
Bear Hug Images, Medical-surgical Nursing Review Questions, Basswood Tree Maine, What Is Castor Seed Used For, Dutch Oven Baked Beans, Stamp Act Cartoon, Mold On Shoes In Closet, Volunteer Hands Clip Art,