Fetching the paper…
Reading the bibliography…
One noted issue of vector-quantized variational autoencoder (VQ-VAE) is that the learned discrete representation uses only a fraction of the full capacity of the codebook, also known as codebook collapse.
Acceleration of stochastic approximation by averaging
Polyak, B. T. and Juditsky, A. B · 1992
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
An introduction to variational methods for graphical models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., and Saul, L. K · 1999
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y., Léonard, N., and Courville, A · 2013
Earlier work this paper cites.
Evaluating speech features with the minimal-pair ABX task: analysis of the classical MFC/PLP pipeline
Schatz, T., Peddinti, V., Bach, F., Jansen, A., Hermansky, H., and Dupoux, E · 2013
Earlier work this paper cites.
Learning phrase representations using RNN encoder–decoder for statistical machine translation
Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y · 2014
Earlier work this paper cites.
Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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.
The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B · 2016
Earlier work this paper cites.
Variable rate image compression with recurrent neural networks
Toderici, G., O’Malley, S. M., Hwang, S. J., Vincent, D., Minnen, D., Baluja, S., Covell, M., and Sukthankar, R · 2016
Earlier work this paper cites.
Pixel recurrent neural networks
van den Oord, A., Kalchbrenner, N., and Kavukcuoglu, K · 2016
Earlier work this paper cites.
Soft-to-hard vector quantization for end-to-end learning compressible representations
Agustsson, E., Mentzer, F., Tschannen, M., Cavigelli, L., Timofte, R., Benini, L., and Gool, L. V · 2017
Earlier work this paper cites.
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
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 · 2017
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2017
Cited alongside, same era.
The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J., Mnih, A., and Teh, Y. W · 2017
Cited alongside, same era.
Lossy image compression with compressive autoencoders
Theis, L., Shi, W., Cunningham, A., and Huszár, F · 2017
Cited alongside, same era.
Neural discrete representation learning
van den Oord, A., Vinyals, O., and Kavukcuoglu, K · 2017
Cited alongside, same era.
CSTR VCTK corpus: English multi-speaker corpus for CSTR Voice Cloning Toolkit
Veaux, C., Yamagishi, J., and MacDonald, K · 2017
Cited alongside, same era.
Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms
MelGAN: Generative adversarial networks for conditional waveform synthesis
Kumar, K., Kumar, R., de Boissiere, T., Gestin, L., Teoh, W. Z., Sotelo, J., de Brébisson, A., Bengio, Y., and Courville, A. C · 2019
Later among the works it cites.
Generating diverse high-fidelity images with VQ-VAE-2
Razavi, A., van den Oord, A., and Vinyals, O · 2019
Later among the works it cites.
InfoVAE: Balancing learning and inference in variational autoencoders
Zhao, S., Song, J., and Ermon, S · 2019
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.
From variational to deterministic autoencoders
Ghosh, P., Sajjadi, M. S., Vergari, A., Black, M., and Schölkopf, B · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Xiao, H., Rasul, K., and Vollgraf, R · 2017
Cited alongside, same era.
PixelSNAIL: An improved autoregressive generative model
Chen, X., Mishra, N., Rohaninejad, M., and Abbeel, P · 2018
Cited alongside, same era.
Hyperspherical variational auto-encoders
Davidson, T. R., Falorsi, L., De Cao, N., Kipf, T., and Tomczak, J. M · 2018
Cited alongside, same era.
Fast decoding in sequence models using discrete latent variables
Kaiser, L., Bengio, S., Roy, A., Vaswani, A., Parmar, N., Uszkoreit, J., and Shazeer, N · 2018
Cited alongside, same era.
Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2018
Cited alongside, same era.
Theory and experiments on vector quantized autoencoders
Roy, A., Vaswani, A., Neelakantan, A., and Parmar, N · 2018
Cited alongside, same era.
Wasserstein auto-encoders
Tolstikhin, I., Bousquet, O., Gelly, S., and Schoelkopf, B · 2018
Cited alongside, same era.
Lee, C.-H., Liu, Z., Wu, L., and Luo, P · 2020
Later among the works it cites.
Deterministic decoding for discrete data in variational autoencoders
Polykovskiy, D. and Vetrov, D · 2020
Later among the works it cites.
Transformer VQ-VAE for unsupervised unit discovery and speech synthesis: ZeroSpeech 2020 challenge
Tjandra, A., Sakti, S., and Nakamura, S · 2020
Later among the works it cites.
Vector-quantized neural networks for acoustic unit discovery in the ZeroSpeech 2020 challenge
van Niekerk, B., Nortje, L., and Kamper, H · 2020
Later among the works it cites.
Hierarchical quantized autoencoder
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.
Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram
Yamamoto, R., Song, E., and Kim, J.-M · 2020
Later among the works it cites.
Taming transformers for high-resolution image synthesis
Esser, P., Rombach, R., and Ommer, B · 2021
Later among the works it cites.
Zero-shot text-to-image generation
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., and Sutskever, I · 2021
Later among the works it cites.
Preventing posterior collapse induced by oversmoothing in Gaussian VAE
Takida, Y., Liao, W.-H., Uesaka, T., Takahashi, S., and Mitsufuji, Y · 2021
Later among the works it cites.