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In this paper, we introduce Random Path Generative Adversarial Network (RPGAN) -- an alternative design of GANs that can serve as a tool for generative model analysis.
The mnist database of handwritten digits
LeCun, Y · 1989
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Learning multiple layers of features from tiny images
Krizhevsky, A. et al · 2009
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Lin, M., Chen, Q., and Yan, S · 2013
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Neural codes for image retrieval
Babenko, A., Slesarev, A., Chigorin, A., and Lempitsky, V · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Understanding deep image representations by inverting them
Mahendran, A. and Vedaldi, A · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F., Zhang, Y., Song, S., Seff, A., and Xiao, J · 2015
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P · 2016
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Generating images with perceptual similarity metrics based on deep networks
Dosovitskiy, A. and Brox, T · 2016
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Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Cited alongside, same era.
Stacked generative adversarial networks
Huang, X., Li, Y., Poursaeed, O., Hopcroft, J., and Belongie, S · 2017
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y · 2018
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Assessing Generative Models via Precision and Recall
Sajjadi, M. S. M., Bachem, O., Lučić, M., Bousquet, O., and Gelly, S · 2018
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Video-to-video synthesis
Wang, T.-C., Liu, M.-Y., Zhu, J.-Y., Liu, G., Tao, A., Kautz, J., and Catanzaro, B · 2018
Later among the works it cites.
Memory replay gans: Learning to generate new categories without forgetting
Wu, C., Herranz, L., Liu, X., van de Weijer, J., Raducanu, B., et al · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
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Unpaired image-to-image translation using cycle-consistent adversarial networks
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Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A · 2017
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Towards the high-quality anime characters generation with generative adversarial networks
Jin, Y., Zhang, J., Li, M., Tian, Y., and Zhu, H · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al · 2017
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Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
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Sgan: An alternative training of generative adversarial networks
Chavdarova, T. and Fleuret, F · 2018
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MGAN: Training generative adversarial nets with multiple generators
Hoang, Q., Nguyen, T. D., Le, T., and Phung, D · 2018
Cited alongside, same era.
Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A · 2018
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Gan dissection: Visualizing and understanding generative adversarial networks
Bau, D., Zhu, J.-Y., Strobelt, H., Bolei, Z., Tenenbaum, J. B., Freeman, W. T., and Torralba, A · 2019
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Large scale GAN training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2019
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A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
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A large-scale study on regularization and normalization in gans
Kurach, K., Lučić, M., Zhai, X., Michalski, M., and Gelly, S · 2019
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Semantic hierarchy emerges in deep generative representations for scene synthesis
Yang, C., Shen, Y., and Zhou, B · 2019
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