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The high dimensionality of images presents architecture and sampling-efficiency challenges for likelihood-based generative models.
Prediction and entropy of printed english
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Stochastic backpropagation and approximate inference in deep generative models
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Imagenet large scale visual recognition challenge
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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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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
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Pixel recurrent neural networks
Van Oord, A., Kalchbrenner, N., and Kavukcuoglu, K · 2016
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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
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CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning
Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Zitnick, C. L., and Girshick, R. B · 2017
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Neural discrete representation learning
Oord, A. v. d., Vinyals, O., and Kavukcuoglu, K · 2017
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Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2018
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Kingma, D. P. and Dhariwal, P · 2018
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Generative pretraining from pixels
Chen, M., Radford, A., Child, R., Wu, J., Jun, H., Luan, D., and Sutskever, I · 2020
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Batch normalization biases residual blocks towards the identity function in deep networks
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Dhariwal, P., Jun, H., Payne, C., Kim, J. W., Radford, A., and Sutskever, I · 2020
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Independent JPEG Group
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Scaling laws for neural language models, 2020
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