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Devising indicative evaluation metrics for the image generation task remains an open problem.
Markov processes over denumerable products of spaces, describing large systems of automata
Vaserstein, L. N · 1969
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The fréchet distance between multivariate normal distributions
Dowson, D. and Landau, B · 1982
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Unbiased look at dataset bias
Torralba, A. and Efros, A. A · 2011
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Better mixing via deep representations
Bengio, Y., Mesnil, G., Dauphin, Y., and Rifai, S · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 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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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2016
Cited alongside, same era.
Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
Cited alongside, same era.
A note on the evaluation of generative models
Theis, L., van den Oord, A., and Bethge, M · 2016
Cited alongside, same era.
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.
Deep image prior
Ulyanov, D., Vedaldi, A., and Lempitsky, V · 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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Pros and cons of gan evaluation measures
Borji, A · 2019
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Adversarial audio synthesis
Donahue, C., McAuley, J., and Puckette, M · 2019
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Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 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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Brock, A., Donahue, J., and Simonyan, K · 2018
Cited alongside, same era.
Nsml: Meet the mlaas platform with a real-world case study
Kim, H., Kim, M., Seo, D., Kim, J., Park, H., Park, S., Jo, H., Kim, K., Yang, Y., Kim, Y., et al · 2018
Cited alongside, same era.
Assessing generative models via precision and recall
Sajjadi, M. S., Bachem, O., Lucic, M., Bousquet, O., and Gelly, S · 2018
Cited alongside, same era.
Improved precision and recall metric for assessing generative models
Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., and Aila, T · 2019
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Revisiting precision recall definition for generative modeling
Simon, L., Webster, R., and Rabin, J · 2019
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