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Learning deep discrete latent presentations offers a promise of better symbolic and summarized abstractions that are more useful to subsequent downstream tasks.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
Earlier work this paper cites.
Visualizing data using t-sne, 2008
van der Maaten, L. and Hinton, G · 2008
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
Earlier work this paper cites.
The mnist database of handwritten digit images for machine learning research [best of the web]
Deng, L · 2012
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons
Bengio, Y · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
Earlier work this paper cites.
Optimal transport for applied mathematicians
Santambrogio, F · 2015
Earlier work this paper cites.
Deep variational information bottleneck
Alemi, A. A., Fischer, I., Dillon, J. V., and Murphy, K · 2016
Earlier work this paper cites.
Stochastic optimization for large-scale optimal transport
Genevay, A., Cuturi, M., Peyré, G., and Bach, F · 2016
Earlier work this paper cites.
beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2016
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2016
Earlier work this paper cites.
Improved variational inference with inverse autoregressive flow
Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., and Welling, M · 2016
Earlier work this paper cites.
Wavenet: A generative model for raw audio
Oord, A. v. d., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K · 2016
Earlier work this paper cites.
Context encoders: Feature learning by inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., and Efros, A. A · 2016
Earlier work this paper cites.
Conditional image generation with pixelcnn decoders
Van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al · 2016
Earlier work this paper cites.
Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
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.
Video pixel networks
Kalchbrenner, N., Oord, A., Simonyan, K., Danihelka, I., Vinyals, O., Graves, A., and Kavukcuoglu, K · 2017
Cited alongside, same era.
Parallel multiscale autoregressive density estimation
Reed, S., Oord, A., Kalchbrenner, N., Colmenarejo, S. G., Wang, Z., Chen, Y., Belov, D., and Freitas, N · 2017
Cited alongside, same era.
wav2vec 2.0: A framework for self-supervised learning of speech representations
Baevski, A., Zhou, Y., Mohamed, A., and Auli, M · 2020
Later among the works it cites.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
Later among the works it cites.
Jukebox: A generative model for music
Dhariwal, P., Jun, H., Payne, C., Kim, J. W., Radford, A., and Sutskever, I · 2020
Later among the works it cites.
Hierarchical quantized autoencoders
Williams, W., Ringer, S., Ash, T., MacLeod, D., Dougherty, J., and Hughes, J · 2020
Later among the works it cites.
Vector quantization-based regularization for autoencoders
Wu, H. and Flierl, M · 2020
Later among the works it cites.
Exploring simple siamese representation learning
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Tolstikhin, I., Bousquet, O., Gelly, S., and Schoelkopf, B · 2017
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Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models
Tucker, G., Mnih, A., Maddison, C. J., Lawson, J., and Sohl-Dickstein, J · 2017
Cited alongside, same era.
Neural discrete representation learning
Van Den Oord, A., Vinyals, O., et al · 2017
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The challenge of realistic music generation: modelling raw audio at scale
Dieleman, S., van den Oord, A., and Simonyan, K · 2018
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Deep encoder-decoder models for unsupervised learning of controllable speech synthesis
Henter, G. E., Lorenzo-Trueba, J., Wang, X., and Yamagishi, J · 2018
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Theory and experiments on vector quantized autoencoders
Roy, A., Vaswani, A., Neelakantan, A., and Parmar, N · 2018
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Variational information bottleneck on vector quantized autoencoders
Wu, H. and Flierl, M · 2018
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Chen, X. and He, K · 2021
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Taming transformers for high-resolution image synthesis
Esser, P., Rombach, R., and Ommer, B · 2021
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Lamda: Label matching deep domain adaptation
Le, T., Nguyen, T., Ho, N., Bui, H., and Phung, D · 2021
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Tidot: a teacher imitation learning approach for domain adaptation with optimal transport
Nguyen, T., Le, T., Dam, N., Tran, Q. H., Nguyen, T., and Phung, D · 2021
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Videogpt: Video generation using vq-vae and transformers
Yan, W., Zhang, Y., Abbeel, P., and Srinivas, A · 2021
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Vector-quantized image modeling with improved vqgan
Yu, J., Li, X., Koh, J. Y., Zhang, H., Pang, R., Qin, J., Ku, A., Xu, Y., Baldridge, J., and Wu, Y · 2021
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Neural topic model via optimal transport
Zhao, H., Phung, D., Huynh, V., Le, T., and Buntine, W · 2021
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A unified wasserstein distributional robustness framework for adversarial training
Bui, A. T., Le, T., Tran, Q. H., Zhao, H., and Phung, D · 2022
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L-verse: Bidirectional generation between image and text
Kim, T., Song, G., Lee, S., Kim, S., Seo, Y., Lee, S., Kim, S. H., Lee, H., and Bae, K · 2022
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SQ-VAE: Variational Bayes on discrete representation with self-annealed stochastic quantization
Takida, Y., Shibuya, T., Liao, W., Lai, C.-H., Ohmura, J., Uesaka, T., Murata, N., Takahashi, S., Kumakura, T., and Mitsufuji, Y · 2022
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Movq: Modulating quantized vectors for high-fidelity image generation
Zheng, C., Vuong, L. T., Cai, J., and Phung, D · 2022
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Unified discrete diffusion for simultaneous vision-language generation
Hu, M., Zheng, C., Zheng, H., Cham, T.-J., Wang, C., Yang, Z., Tao, D., and Suganthan, P. N · 2023
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