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Q /Parent 1 0 R /Resources << [ (GAN) -516.011 (is) -516.998 (the) -515.984 (coupling) -515.996 (of) -517 (tr) 14.9914 (anslation) -516.018 (with) -517.019 (discrimination) ] TJ >> /F2 102 0 R Additionally, this paper provides many additional techniques designed to stabilize the training of DCGANs. /R19 9.9626 Tf /Annots [ ] q between distributions. 71.715 5.789 67.215 10.68 67.215 16.707 c

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The combination of the mapping network and the distribution of AdaIN conditioning throughout the generator model makes this pretty difficult to implement yourself, but it is still a great read and contains many interesting ideas. /R21 5.9776 Tf >>

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The StackGAN model works similar to Progressively Growing GANs in the sense that it works on multiple scales.

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T* endobj /R44 61 0 R 11.9563 TL >> The StackGAN is very unique to the other papers because it goes from natural language text to image. /Resources << >> (tsinghua\056edu\056cn) Tj This article will list 10 papers on GANs that will give you a great introduction to GAN as well as a foundation for understanding the state-of-the-art. Q f*

/Contents 50 0 R /R9 11.9552 Tf extensive theoretical work highlighting the deep connections to other distances q '!�딱�����s,�nF�'U���_���!p4��;��E�H��_QZ��~{��}�u�X��pwU��?����e_�ߨ��Ǐ{�g���Rq�B��Ǎì�>��܆1ќ�^��q��X�^=@c���$DDz�/ks�T*�=Y=��c�j����K}fv(o�h��`�$�z������?�غ�f^�l.�0��U||��� BT 270 32 72 14 re CycleGAN more specifically deals with the case of image-to-image translation where you do not have paired training samples.

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>> GAN instability is largely increased with respect to the target image resolution size, and this paper shows a workaround to this problem.

All of these additional topics are bound to come up in your GAN research.

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T* /MediaBox [ 0 0 612 792 ] This paper also gives the derivation for the optimal discriminator, a proof which frequently comes up in the more recent GAN papers. >> /ExtGState << << /Count 10 87.273 33.801 l

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However, it is a great paper to read simply due to the elegance of the Cycle-Consistency loss formulation and the intuition on how this stabilizes GAN training. >> ET T* /R8 20 0 R >> [ (Image\055to\055Image) -477.003 (translation) -476 (transforming) -477.018 (images) -476.996 (from) ] TJ [ (can) -315.982 (we) -316.016 (c) 15.0122 (hang) 10.0179 (e) ] TJ /R13 7.9701 Tf

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Add a 100.875 14.996 l /R15 8.9664 Tf The StackGAN first outputs an image of resolution 64² and then takes this as prior information to generate an image of resolution 256².

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/R11 22 0 R In summation, the DCGAN paper is a must-read GAN paper because it defines the architecture in such a clear way that it is easy to get started with some code and begin developing an intuition for GANs. /R63 95 0 R 73.607 -13.9477 Td

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Furthermore, /Annots [ ]

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-41.482 -17.9332 Td This paper uses a multi-scale architecture where the GAN builds up from 4² to 8² and up to 1024² resolution. /Resources <<

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Generative Adversarial Networks are one of the most interesting and popular applications of Deep Learning. This paper shows how convolutional layers can be used with GANs and provides a series of additional architectural guidelines for doing this. [ (1\056) -249.99 (Intr) 18.0146 (oduction) ] TJ 10 0 0 10 0 0 cm

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Soumith Chintala

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Martin Arjovsky /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] /ca 1

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This paper contends a novel role of the dis-criminator by reusing it for encoding the images of the tar-get domain. /R79 105 0 R This is done by altering a text embedding such that it captures visual characteristics. T* These include feature matching, minibatch discrimination, historical averaging, one-sided label smoothing, and virtual batch normalization. endobj stream [ (criminator) -261.988 (by) -263.014 (r) 37.0196 (eusing) -262.01 (it) -263 (for) -262.016 (encoding) -262.992 (the) -261.988 (ima) 10.013 (g) 10.0032 (es) -263.012 (of) -261.983 (the) -263.008 (tar) 20.0138 (\055) ] TJ /x6 Do

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