SSDL: Self-Supervised Dictionary Learning

• URL: http://arxiv.org/abs/2112.01790v1
• Date: Fri, 3 Dec 2021 08:55:08 GMT
• Title: SSDL: Self-Supervised Dictionary Learning
• Authors: Shuai Shao, Lei Xing, Wei Yu, Rui Xu, Yanjiang Wang, Baodi Liu
• Abstract summary: We propose a Self-Supervised Dictionary Learning (SSDL) framework to address this challenge. Specifically, we first design a $p$-Laplacian Attention Hypergraph Learning block as the pretext task to generate pseudo soft labels for DL. Then, we adopt the pseudo labels to train a dictionary from a primary label-embedded DL method.
• Score: 20.925371262076744
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The label-embedded dictionary learning (DL) algorithms generate influential dictionaries by introducing discriminative information. However, there exists a limitation: All the label-embedded DL methods rely on the labels due that this way merely achieves ideal performances in supervised learning. While in semi-supervised and unsupervised learning, it is no longer sufficient to be effective. Inspired by the concept of self-supervised learning (e.g., setting the pretext task to generate a universal model for the downstream task), we propose a Self-Supervised Dictionary Learning (SSDL) framework to address this challenge. Specifically, we first design a $p$-Laplacian Attention Hypergraph Learning (pAHL) block as the pretext task to generate pseudo soft labels for DL. Then, we adopt the pseudo labels to train a dictionary from a primary label-embedded DL method. We evaluate our SSDL on two human activity recognition datasets. The comparison results with other state-of-the-art methods have demonstrated the efficiency of SSDL.

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