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Diffusion-based methods represented as stochastic differential equations on a continuous-time domain have recently proven successful as a non-adversarial generative model.
Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 1907
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Reverse-time diffusion equation models
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Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
Rousseeuw, P. J · 1987
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Signature verification using a ”siamese” time delay neural network
Bromley, J., Bentz, J., Bottou, L., Guyon, I., Lecun, Y., Moore, C., Sackinger, E., and Shah, R · 1993
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Estimation of non-normalized statistical models by score matching
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Big self-supervised models are strong semi-supervised learners
Chen, T., Kornblith, S., Swersky, K., Norouzi, M., and Hinton, G. E · 2006
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MNIST handwritten digit database
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A connection between score matching and denoising autoencoders
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Representation learning: A review and new perspectives
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Adaptive noise schedule for denoising autoencoder
Chandra, B. and Sharma, R · 2014
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Stochastic backpropagation and approximate inference in deep generative models
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