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Learning Deep Representations Using Convolutional Auto-encoders with Symmetric Skip Connections
[article]
2017
arXiv
pre-print
Unsupervised pre-training was a critical technique for training deep neural networks years ago. With sufficient labeled data and modern training techniques, it is possible to train very deep neural networks from scratch in a purely supervised manner nowadays. However, unlabeled data is easier to obtain and usually of very large scale. How to make use of them better to help supervised learning is still a well-valued topic. In this paper, we investigate convolutional denoising auto-encoders to
arXiv:1611.09119v2
fatcat:uijwlfho3bc2zm2c7nje23mlx4