Learning Deep Autoencoders without Layer-wise Training.
Year: 2014
Type: preprint
Abstract: Although greedy layer-wise pre-training has achieved a lot of success in deep neural networks, due to the locality of the method, higher layers of the network may not learn representations that are useful for the original input. In this work, a novel unsupervised joint training method that tries to integrate multiple single layer training objectives into one global objective is proposed. It not only mimics the layer-wise training scheme locally, but also adjusts all the weights together based on the reconstruction loss from end-to-end. Results show that it extracts more representative features and achieves better classification results in the un-supervised setting, and also achieves comparable performance in the supervised setting as compared to the greedy layer-wise approach while being faster. 1
Source: arXiv (Cornell University)
Author: Yingbo Zhou
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1789045941