VAE-Info-cGAN-Reference-Cited by-同舟云学术

VAE-Info-cGAN

Published:2020-11-03 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 13th ACM SIGSPATIAL International Workshop on Computational Transportation Science
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Author:

Xiao Xuerong¹,Ganguli Swetava²,Pandey Vipul²

Affiliation:

1. Stanford University

2. Apple

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3423457.3429361

Reference29 articles.

1. C. Bowles etal 2018. GAN augmentation: Augmenting training data using Generative Adversarial Networks. arXiv preprint arXiv:1810.10863 (2018). C. Bowles et al. 2018. GAN augmentation: Augmenting training data using Generative Adversarial Networks. arXiv preprint arXiv:1810.10863 (2018).

2. A. Brock etal 2018. Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 (2018). A. Brock et al. 2018. Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 (2018).

3. C. P. Burgess etal 2018. Understanding disentangling in β-VAE. arXiv preprint arXiv:1804.03599 (2018). C. P. Burgess et al. 2018. Understanding disentangling in β-VAE. arXiv preprint arXiv:1804.03599 (2018).

4. Everybody Dance Now

5. X. Chen etal 2016. InfoGAN: Interpretable representation learning by information maximizing Generative Adversarial Nets. X. Chen et al. 2016. InfoGAN: Interpretable representation learning by information maximizing Generative Adversarial Nets.

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