Unsupervised Deep Learning by Injecting Low-Rank and Sparse Priors

• URL: http://arxiv.org/abs/2106.10923v1
• Date: Mon, 21 Jun 2021 08:41:02 GMT
• Title: Unsupervised Deep Learning by Injecting Low-Rank and Sparse Priors
• Authors: Tomoya Sakai
• Abstract summary: We focus on employing sparsity-inducing priors in deep learning to encourage the network to concisely capture the nature of high-dimensional data. We demonstrate unsupervised learning of U-Net for background subtraction using low-rank and sparse priors.
• Score: 5.5586788751870175
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: What if deep neural networks can learn from sparsity-inducing priors? When the networks are designed by combining layer modules (CNN, RNN, etc), engineers less exploit the inductive bias, i.e., existing well-known rules or prior knowledge, other than annotated training data sets. We focus on employing sparsity-inducing priors in deep learning to encourage the network to concisely capture the nature of high-dimensional data in an unsupervised way. In order to use non-differentiable sparsity-inducing norms as loss functions, we plug their proximal mappings into the automatic differentiation framework. We demonstrate unsupervised learning of U-Net for background subtraction using low-rank and sparse priors. The U-Net can learn moving objects in a training sequence without any annotation, and successfully detect the foreground objects in test sequences.

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