[ (xudonmao\100gmail\056com\054) -599.99200 (itqli\100cityu\056edu\056hk\054) -599.99200 (hrxie2\100gmail\056com) ] TJ /R10 39 0 R [ (4) -0.30019 ] 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 /S /Transparency T* /R34 69 0 R /F1 224 0 R [ (1) -0.30019 ] TJ endobj In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. n /Filter /FlateDecode 8 0 obj Please cite the above paper … [ (belie) 24.98600 (v) 14.98280 (e) -315.99100 (the) 14.98520 (y) -315.00100 (are) -315.99900 (from) -316.01600 (real) -315.01100 (data\054) -332.01800 (it) -316.01100 (will) -316.00100 (cause) -315.00600 (almost) -315.99100 (no) -316.01600 (er) 19.98690 (\055) ] TJ /Type /Catalog 11.95510 TL 14 0 obj /R12 7.97010 Tf T* /R114 188 0 R >> [ (3) -0.30091 ] TJ

In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection. [ <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 [ (3) -0.30019 ] TJ /ProcSet [ /Text /ImageC /ImageB /PDF /ImageI ] endobj /Font << In this paper, we introduce two novel mechanisms to address above mentioned problems. [ (5) -0.29911 ] TJ >> /F2 9 Tf /Contents 192 0 R In this work, … 20 0 obj -11.95510 -11.95470 Td We propose a novel framework for generating realistic time-series data that combines … /Type /Pages /Subtype /Form /Count 9 /R20 63 0 R This is actually a neural network that incorporates data from preparation and uses current data and information to produce entirely new data. /Resources 19 0 R 34.34730 -38.45700 Td To address these issues, in this paper, we propose a novel approach termed FV-GAN to finger vein extraction and verification, based on generative adversarial network (GAN), as the first attempt in this area. q /R7 32 0 R 11.95590 TL Learn more. T* /Title (Least Squares Generative Adversarial Networks) ArXiv 2014. /R12 44 0 R /R18 59 0 R >> 258.75000 417.59800 Td << /R42 86 0 R 11.95510 TL /x6 Do /F2 190 0 R /R50 108 0 R "Generative Adversarial Networks." 19.67700 -4.33906 Td We propose a novel, two-stage pipeline for generating synthetic medical images from a pair of generative adversarial networks, tested in practice on retinal fundi images. 11.95590 TL 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 sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. 9 0 obj /MediaBox [ 0 0 612 792 ] >> /Font << >> The classifier serves as a generator that generates … /ExtGState << >> GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. /Parent 1 0 R In this paper, we present an unsupervised image enhancement generative adversarial network (UEGAN), which learns the corresponding image-to-image mapping from a set of images with desired characteristics in an unsupervised manner, rather than learning on a large number of paired images. /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] To address these issues, in this paper, we propose a novel approach termed FV-GAN to finger vein extraction and verification, based on generative adversarial network (GAN), as the first attempt in this area. << /R50 108 0 R /R145 200 0 R First, LSGANs are able to Abstract

Consider learning a policy from example expert behavior, without interaction with the expert … /Subtype /Form T* -72.89920 -8.16758 Td /Resources << ArXiv 2014. /CS /DeviceRGB /R8 55 0 R That is, we utilize GANs to train a very powerful generator of facial texture in UV space. endobj /R12 7.97010 Tf /Rotate 0 GANs have made steady progress in unconditional image generation (Gulrajani et al., 2017; Karras et al., 2017, 2018), image-to-image translation (Isola et al., 2017; Zhu et al., 2017; Wang et al., 2018b) and video-to-video synthesis (Chan et al., 2018; Wang … Please cite this paper if you use the code in this repository as part of a published research project. [ (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 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 … Q /Font << /ProcSet [ /Text /ImageC /ImageB /PDF /ImageI ] /XObject << Inspired by recent successes in deep learning we propose a novel approach to anomaly detection using generative adversarial networks. << >> Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio. /R137 211 0 R -15.24300 -11.85590 Td /Contents 66 0 R /MediaBox [ 0 0 612 792 ] Adversarial Networks. We propose an adaptive discriminator augmentation mechanism that … >> [ (Raymond) -249.98700 (Y) 129 (\056K\056) -250 (Lau) ] TJ >> You can always update your selection by clicking Cookie Preferences at the bottom of the page. /CA 1 T* At the same time, supervised models for sequence prediction - which allow finer control over network dynamics - are inherently deterministic. /s7 36 0 R [ (1\056) -249.99000 (Intr) 18.01460 (oduction) ] TJ /R52 111 0 R >> /Length 53008 There are two bene・》s of … [ (ror) -335.98600 (because) -335.98600 (the) 14.98520 (y) -334.99800 (are) -335.99500 (on) -336.01300 (the) -336.01300 (correct) -335.98800 (side\054) -356.98500 (i\056e\056\054) -356.98900 (the) -336.01300 (real) -335.98800 (data) ] TJ -50.60900 -8.16758 Td /R85 172 0 R [ (4) -0.30091 ] TJ /R71 130 0 R /Type /XObject /F2 89 0 R q [ (Stephen) -250.01200 (P) 15.01580 (aul) -250 (Smolle) 15.01370 (y) ] TJ 19.67620 -4.33789 Td >> Part of Advances in Neural Information Processing Systems 29 (NIPS 2016) Bibtex » Metadata » Paper » Reviews » Supplemental » Authors. We use 3D fully convolutional networks to form the generator, which can better model the 3D spatial information and thus could solve the … /Group << ️ [Energy-based generative adversarial network] (Lecun paper) ️ [Improved Techniques for Training GANs] (Goodfellow's paper) ️ [Mode Regularized Generative Adversarial Networks] (Yoshua Bengio , ICLR 2017) ️ [Improving Generative Adversarial Networks with Denoising Feature Matching] First, we introduce a hybrid GAN (hGAN) consisting of a 3D generator network and a 2D discriminator network for deep MR to CT synthesis using unpaired data. /R62 118 0 R [ (Qing) -250.00200 (Li) ] TJ 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. /R10 11.95520 Tf 17 0 obj /R140 214 0 R 4 0 obj /ca 1 Download PDF Abstract: Previous works (Donahue et al., 2018a; Engel et al., 2019a) have found that generating coherent raw audio waveforms … /Type /Page /R54 102 0 R Title: MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis. /Annots [ ] [ (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 /R20 63 0 R /R58 98 0 R >> T* T* /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] To bridge the gaps, we conduct so far the most comprehensive experimental study that investigates apply-ing GAN to relational data synthesis. generative adversarial networks (GANs) (Goodfellow et al., 2014). /R79 123 0 R We use 3D fully convolutional networks to form the … In this paper, we take a radically different approach and harness the power of Generative Adversarial Networks (GANs) and DCNNs in order to reconstruct the facial texture and shape from single images. 2 0 obj /R62 118 0 R /F2 183 0 R /R10 10.16190 Tf Awesome papers about Generative Adversarial Networks. q Don't forget to have a look at the supplementary as well (the Tensorflow FIDs can be found there (Table S1)). As shown by the right part of Figure 2, NaGAN consists of a classifier and a discriminator. /R12 7.97010 Tf That is, we utilize GANs to train a very powerful generator of facial texture in UV space. /x10 23 0 R endobj Quantum machine learning is expected to be one of the first potential general-purpose applications of near-term quantum devices. First, we introduce a hybrid GAN (hGAN) consisting of a 3D generator network and a 2D discriminator network for deep MR to CT synthesis using unpaired data. Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio. /R50 108 0 R /R10 11.95520 Tf 3 0 obj /R10 39 0 R Majority of papers are related to Image Translation. In this paper, we propose a Distribution-induced Bidirectional Generative Adversarial Network (named D-BGAN) for graph representation learning. The code allows the users to reproduce and extend the results reported in the study. 19.67620 -4.33906 Td >> 0.50000 0.50000 0.50000 rg /Length 28 /R29 77 0 R We use essential cookies to perform essential website functions, e.g. 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 … [ (ef) 25.00810 (fecti) 25.01790 (v) 14.98280 (eness) -249.99000 (of) -249.99500 (these) -249.98800 (models\056) ] TJ /R42 86 0 R endstream /ExtGState << [ (generati) 24.98420 (v) 14.98280 (e) -315.99100 (models\054) -333.00900 (obtain) -316.00100 (limited) -315.98400 (impact) -316.00400 (from) -316.99600 (deep) -315.98400 (learn\055) ] TJ If nothing happens, download GitHub Desktop and try again. The task is designed to answer the question: given an audio clip spoken by an unseen person, can we picture a face that has as many common elements, or associations as possible with the speaker, in terms of identity?

To address … [ (1) -0.30091 ] TJ /R14 48 0 R q /R20 63 0 R /R10 11.95520 Tf Part of Advances in Neural Information Processing Systems 27 (NIPS 2014) Bibtex » Metadata » Paper » Reviews » Authors. 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. 11.95510 -17.51600 Td /a0 << >> << << /R7 32 0 R 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 synthetic images. /R42 86 0 R /CA 1 /Type /Group The code allows the users to reproduce and extend the results reported in the study. /Type /XObject /I true [ (works) -220.99600 (\050GANs\051) -221.00200 (has) -221.00600 (pr) 44.98390 (o) 10.00320 (ven) -220.98600 (hug) 10.01300 (ely) -220.98400 (successful\056) -301.01600 (Re) 39.99330 (gular) -220.99300 (GANs) ] TJ T* /BBox [ 133 751 479 772 ] /R87 155 0 R /Contents 185 0 R >> T* /XObject << /Filter /FlateDecode T* /R8 55 0 R 4.02227 -3.68828 Td T* /s5 gs [ (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 [ (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 /s5 33 0 R T* 11.95510 TL 11.95590 TL /R18 59 0 R 4.02227 -3.68828 Td /R60 115 0 R /Subtype /Form [ (the) -253.00900 (f) 9.99588 (ak) 9.99833 (e) -254.00200 (samples) -252.99000 (are) -254.00900 (from) -253.00700 (real) -254.00200 (data\056) -320.02000 (So) -252.99700 (f) 9.99343 (ar) 39.98350 (\054) -255.01100 (plenty) -252.99200 (of) -253.99700 (w) 10.00320 (orks) ] TJ << /s9 26 0 R /Type /Page q 1 0 0 1 149.80500 675.06700 Tm Unlike the CNN-based methods, FV-GAN learns from the joint distribution of finger vein images and … x�e�� AC����̬wʠ� ��=p���,?��]%���+H-lo�䮬�9L��C>�J��c���� ��"82w�8V�Sn�GW;�" Abstract

Consider learning a policy from example expert behavior, without interaction with the expert or access to a reinforcement signal. endobj [ (2) -0.50062 ] TJ [ (2) -0.30001 ] TJ Our method takes unpaired photos and cartoon images for training, which is easy to use. /Filter /FlateDecode /R42 86 0 R [ (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 The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images … q endobj A major recent breakthrough in classical machine learning is the notion of generative adversarial … /R8 55 0 R >> 47.57190 -37.85820 Td [ (Xudong) -250.01200 (Mao) ] TJ stream /ca 1 >> T* Q %PDF-1.3 /Subject (2017 IEEE International Conference on Computer Vision) >> 11.95510 -19.75900 Td /R40 90 0 R We show that minimizing the objective function of LSGAN yields mini- mizing the Pearsonマ・/font>2divergence. 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Part of Advances in Neural Information Processing Systems 29 (NIPS 2016) Bibtex » Metadata » Paper » Reviews » Supplemental » Authors. T* /BBox [ 67 752 84 775 ] In this paper, we present an unsupervised image enhancement generative adversarial network (UEGAN), which learns the corresponding image-to-image mapping from a set of images with desired characteristics in an unsupervised manner, rather than learning on a large number of paired images. /R18 59 0 R /Parent 1 0 R /R8 55 0 R To overcome such a prob- lem, we propose in this paper the Least Squares Genera- tive Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator. >> We develop a hierarchical generation process to divide the complex image generation task into two parts: geometry and photorealism. 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T* /R12 6.77458 Tf [ (samples\073) -281.99700 (while) -272.01600 (the) -271.98600 (generator) -271.00900 (tries) -271.97900 (to) -271.00400 (generate) -271.99900 (f) 9.99343 (ak) 9.99833 (e) -271.99900 (samples) ] TJ stream >> -11.95510 -11.95510 Td If nothing happens, download Xcode and try again. /R89 135 0 R /R93 152 0 R /Resources << endobj Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. stream First, we illustrate improved performance on tumor segmentation by leveraging the synthetic images as a form of data … >> /R60 115 0 R /Length 228 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. ET /R7 32 0 R >> /Rotate 0 [ (raylau\100cityu\056edu\056hk\054) -600.00400 (zhenwang0\100gmail\056com\054) -600.00400 (steve\100codehatch\056com) ] TJ endstream >> [ (minimizing) -411.99300 (the) -410.98300 (objective) -411.99500 (function) -410.99300 (of) -411.99700 (LSGAN) -410.99000 (yields) -411.99300 (mini\055) ] TJ f 7 0 obj /F1 227 0 R Activation Functions): If no match, add ... Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. [ (vised) -316.00600 (learning) -316.98900 (tasks\056) -508.99100 (Unl) 0.99493 (ik) 10.00810 (e) -317.01100 (other) -316.01600 (deep) -315.98600 (generati) 24.98600 (v) 14.98280 (e) -317.01100 (models) ] TJ >> /Subtype /Form 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. Generative Adversarial Nets. 19 0 obj /S /Transparency /R18 59 0 R /R135 209 0 R -94.82890 -11.95510 Td Given a training set, this technique learns to generate new data with the same statistics as the training set. /MediaBox [ 0 0 612 792 ] The method was developed by Ian Goodfellow in 2014 and is outlined in the paper Generative Adversarial Networks.The goal of a GAN is to train a discriminator to be able to distinguish between real and fake data … >> 11.95510 TL T* BT -83.92770 -24.73980 Td Awesome paper list with code about generative adversarial nets. /R14 10.16190 Tf >> 18 0 obj >> -137.17000 -11.85590 Td T* [ (LSGANs) -370.01100 (ar) 36.98520 (e) -371.00100 (of) -370.00400 (better) -370 (quality) -369.98500 (than) -371.01400 (the) -370.00900 (ones) -370.00400 (g) 10.00320 (ener) 15.01960 (ated) -370.98500 (by) ] TJ 144.50300 -8.16797 Td /R8 14.34620 Tf BT T* If nothing happens, download the GitHub extension for Visual Studio and try again. endstream We propose a novel, two-stage pipeline for generating synthetic medical images from a pair of generative adversarial networks, tested in practice on retinal fundi images. /Resources << /Font << /R18 9.96260 Tf /Type /Group /Parent 1 0 R /Group << ️ [Energy-based generative adversarial network] (Lecun paper) ️ [Improved Techniques for Training GANs] (Goodfellow's paper) ️ [Mode Regularized Generative Adversarial Networks] (Yoshua Bengio , ICLR 2017) ️ [Improving Generative Adversarial Networks with Denoising Feature Matching] /ExtGState << [ (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 T* /R148 208 0 R 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 … >> endobj T* /R16 9.96260 Tf [ (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 We present Time-series Generative Adversarial Networks (TimeGAN), a natural framework for generating realistic time-series data in various domains. /ca 1 92.75980 4.33789 Td /R40 90 0 R PyTorch implementation of the CVPR 2020 paper "A U-Net Based Discriminator for Generative Adversarial Networks". /F2 134 0 R 10 0 obj [ (stability) -249.98900 (of) -249.98500 (LSGANs\056) ] TJ /Type /Page 11.95510 TL T* [ (which) -265 (adopt) -264.99700 (the) -265.00700 (least) -263.98300 (squares) -265.00500 (loss) -264.99000 (function) -264.99000 (for) -265.01500 (the) -265.00500 (discrim\055) ] TJ At the same time, supervised models for sequence prediction - which allow finer control over network dynamics - are inherently deterministic. /R139 213 0 R /s7 gs << /Length 28 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 … >> /ExtGState << /R10 39 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 Our method takes unpaired photos and cartoon images for training, which is easy to use. Generative Adversarial Networks, or GANs for short, were first described in the 2014 paper by Ian Goodfellow, et al. /Font << /R40 90 0 R /MediaBox [ 0 0 612 792 ] [ (W) 91.98650 (e) -242.00300 (e) 15.01280 (valuate) -242.01700 (LSGANs) -241.99300 (on) -241.98900 (LSUN) -242.00300 (and) -243.00400 (CIF) 115.01500 (AR\05510) -241.98400 (datasets) -242.00100 (and) ] TJ For more information, see our Privacy Statement. << /x18 15 0 R T* /ExtGState << [ (CodeHatch) -250.00200 (Corp\056) ] TJ /Filter /FlateDecode << /Length 17364 PyTorch implementation of the CVPR 2020 paper "A U-Net Based Discriminator for Generative Adversarial Networks". [49], we first present a naive GAN (NaGAN) with two players. /ProcSet [ /Text /ImageC /ImageB /PDF /ImageI ] Q /MediaBox [ 0 0 612 792 ] /R54 102 0 R Several recent work on speech synthesis have employed generative adversarial networks (GANs) to produce raw waveforms. /Font << /R10 39 0 R x�+��O4PH/VЯ0�Pp�� >> [ (Unsupervised) -309.99100 (learning) -309.99100 (with) -309.99400 (g) 10.00320 (ener) 15.01960 (ative) -310.99700 (adver) 10.00570 (sarial) -309.99000 (net\055) ] TJ [ (\037) -0.69964 ] TJ /ExtGState << T* 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�����=�/������ /F1 191 0 R endobj /x10 Do 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. T* generative adversarial networks (GANs) (Goodfellow et al., 2014). /F2 226 0 R << Generative Adversarial Imitation Learning. [�R� �h�g��{��3}4/��G���y��YF:�!w�}��Gn+���'x�JcO9�i�������뽼�_-:`� [ (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 To bridge the gaps, we conduct so far the most comprehensive experimental study … The paper and supplementary can be found here. Paper where method was first introduced: ... Quantum generative adversarial networks. 4.02227 -3.68789 Td /x6 17 0 R 6 0 obj /Subtype /Form /R12 7.97010 Tf /R62 118 0 R /R56 105 0 R /F1 47 0 R The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. 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 implicitly bridges the graph and feature spaces by prototype learning. /R8 55 0 R >> /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] 11.95510 -17.51720 Td We … T* /Type /Page ET 11 0 obj >> [ (mizing) -327.99100 (the) -328.01600 (P) 79.99030 (ear) 10.00570 (son) ] TJ Two novel losses suitable for cartoonization are pro-posed: (1) a semantic content loss, which is formulated as Generative adversarial networks (GAN) provide an alternative way to learn the true data distribution. data synthesis using generative adversarial networks (GAN) and proposed various algorithms. >> The proposed … /I true 1 0 0 1 297 35 Tm We evaluate the perfor- mance of the network by leveraging a closely related task - cross-modal match-ing. /MediaBox [ 0 0 612 792 ] The results show that … 4.02305 -3.68750 Td /R54 102 0 R /Type /XObject Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. 11.95510 TL /Filter /FlateDecode /R95 158 0 R T* [ (Department) -249.99300 (of) -250.01200 (Computer) -250.01200 (Science\054) -249.98500 (City) -250.01400 (Uni) 25.01490 (v) 15.00120 (ersity) -250.00500 (of) -250.01200 (Hong) -250.00500 (K) 35 (ong) ] TJ /R12 44 0 R << >> stream q >> [ (Department) -249.99400 (of) -250.01100 (Mathematics) -250.01400 (and) -250.01700 (Information) -250 (T) 69.99460 (echnology) 64.98290 (\054) -249.99000 (The) -249.99300 (Education) -249.98100 (Uni) 25.01490 (v) 15.00120 (ersity) -250.00500 (of) -250.00900 (Hong) -250.00500 (K) 35 (ong) ] TJ [ (resolution) -499.99500 (\13316\135\054) -249.99300 (and) -249.99300 (semi\055supervised) -249.99300 (learning) -500.01500 (\13329\135\056) ] TJ 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. /Type /Page /R40 90 0 R Despite stability issues [35, 2, 3, 29], they were shown to be capable of generating more realistic and sharper images than priorapproachesandtoscaletoresolutionsof1024×1024px /R10 39 0 R >> /XObject << [ (this) -246.01200 (loss) -246.99300 (function) -246 (may) -247.01400 (lead) -245.98600 (to) -245.98600 (the) -247.01000 (vanishing) -245.99600 (gr) 14.99010 (adients) -246.98600 (pr) 44.98510 (ob\055) ] TJ Inspired by recent successes in deep learning we propose a novel approach to anomaly detection using generative adversarial networks. /R77 161 0 R /Rotate 0 >> /R136 210 0 R [ (Zhen) -249.99100 (W) 80 (ang) ] TJ However, the hallucinated details are often accompanied with unpleasant artifacts. /CA 1 /R12 7.97010 Tf (Abstract) Tj [ (ious) -395.01000 (tasks\054) -431.00400 (such) -394.98100 (as) -394.99000 (image) -395.01700 (generation) -790.00500 (\13321\135\054) -430.98200 (image) -395.01700 (super) 20.00650 (\055) ] TJ

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