Conditional Image Synthesis with Auxiliary Classifier GANs
The discriminator D receives as input either a training image or a synthesized image from the generator and outputs a probability distri-bution P(SjX) = D(X) over possible image sources. The discriminator is trained to maximize the log-likelihood it assigns to the correct source: L= E[logP(S= real jX real)]+ E[logP(S= fakejX fake)] (1)
Download Conditional Image Synthesis with Auxiliary Classifier GANs
Information
Domain:
Source:
Link to this page:
Documents from same domain
![arXiv:0706.3639v1 [cs.AI] 25 Jun 2007](/cache/preview/4/1/3/9/3/1/4/b/thumb-4139314b93ef86b7b4c2d05ebcc88e46.jpg)
arXiv:0706.3639v1 [cs.AI] 25 Jun 2007
arxiv.orgarXiv:0706.3639v1 [cs.AI] 25 Jun 2007 Technical Report IDSIA-07-07 A Collection of Definitions of Intelligence Shane Legg IDSIA, Galleria …
Deep Residual Learning for Image Recognition - …
arxiv.orgDeep Residual Learning for Image Recognition Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research fkahe, v-xiangz, v-shren, jiansung@microsoft.com
![arXiv:1301.3781v3 [cs.CL] 7 Sep 2013](/cache/preview/4/d/5/0/4/3/4/0/thumb-4d504340120163c0bdf3f4678d8d217f.jpg)
arXiv:1301.3781v3 [cs.CL] 7 Sep 2013
arxiv.orgFor all the following models, the training complexity is proportional to O = E T Q; (1) where E is number of the training epochs, T is the number of …
![@google.com arXiv:1609.03499v2 [cs.SD] 19 Sep 2016](/cache/preview/c/3/4/9/4/6/9/b/thumb-c349469b499107d21e221f2ac908f8b2.jpg)
@google.com arXiv:1609.03499v2 [cs.SD] 19 Sep 2016
arxiv.orgwhere 1 <x t <1 and = 255. This non-linear quantization produces a significantly better reconstruction than a simple linear quantization scheme. …
A Tutorial on UAVs for Wireless Networks: …
arxiv.orgA Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems Mohammad Mozaffari 1, ... to UAVs in wireless communications is the work in …
Adversarial Generative Nets: Neural Network …
arxiv.orgAdversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer Carnegie Mellon University
Massive Exploration of Neural Machine Translation ...
arxiv.orgMassive Exploration of Neural Machine Translation Architectures Denny Britzy, Anna Goldie, Minh-Thang Luong, Quoc Le fdennybritz,agoldie,thangluong,qvlg@google.com Google Brain
Mastering Chess and Shogi by Self-Play with a …
arxiv.orgMastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm David Silver, 1Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, 1Matthew Lai, Arthur Guez, Marc Lanctot,1
Going deeper with convolutions - arXiv
arxiv.orgGoing deeper with convolutions Christian Szegedy Google Inc. Wei Liu University of North Carolina, Chapel Hill Yangqing Jia Google Inc. Pierre Sermanet
Andrew G. Howard Menglong Zhu Bo Chen Dmitry ...
arxiv.orgMobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto Hartwig Adam
Related documents
Labels to Street Scene Labels to Facade BW to Color
arxiv.orgdiscriminator, D, learns to classify between fake (synthesized by the generator) and real fedge, photogtuples. The generator, G, learns to fool the discriminator. Unlike an unconditional GAN, both the generator and discriminator observe the input edge map. large …
![arXiv:2108.02774v1 [cs.CV] 5 Aug 2021](/cache/preview/3/a/d/f/b/9/3/2/thumb-3adfb9327add494116d19fc92cd00759.jpg)
arXiv:2108.02774v1 [cs.CV] 5 Aug 2021
arxiv.orgerator and discriminator pair using transfer learning [13, 71]. The fine-tuning can improve upon training from scratch [67], but it can also easily overfit on the new training data. To avoid overfitting, several groups propose to limit the changes in model weights: Batch Statistic Adaptation preserves all