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The field of deep generative modeling has succeeded in producing astonishingly realistic-seeming images and audio, but quantitative evaluation remains a challenge.
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Ballé, J., Minnen, D., Singh, S., Hwang, S. J., and Johnston, N · 2018
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An empirical study on evaluation metrics of generative adversarial networks, 2018
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Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Rezende, D. J. and Viola, F · 2018
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Large scale GAN training for high fidelity natural image synthesis
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Bit-swap: Recursive bits-back coding for lossless compression with hierarchical latent variables
Kingma, F. H., Abbeel, P., and Ho, J · 2019
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Townsend, J., Bird, T., and Barber, D · 2019
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Improving inference for neural image compression
Yang, Y., Bamler, R., and Mandt, S · 2020
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