Improving Few-Shot Learning with Auxiliary Self-Supervised Pretext Tasks

• URL: http://arxiv.org/abs/2101.09825v1
• Date: Sun, 24 Jan 2021 23:21:43 GMT
• Title: Improving Few-Shot Learning with Auxiliary Self-Supervised Pretext Tasks
• Authors: Nathaniel Simard and Guillaume Lagrange
• Abstract summary: Recent work on few-shot learning shows that quality of learned representations plays an important role in few-shot classification performance. On the other hand, the goal of self-supervised learning is to recover useful semantic information of the data without the use of class labels. We exploit the complementarity of both paradigms via a multi-task framework where we leverage recent self-supervised methods as auxiliary tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent work on few-shot learning \cite{tian2020rethinking} showed that quality of learned representations plays an important role in few-shot classification performance. On the other hand, the goal of self-supervised learning is to recover useful semantic information of the data without the use of class labels. In this work, we exploit the complementarity of both paradigms via a multi-task framework where we leverage recent self-supervised methods as auxiliary tasks. We found that combining multiple tasks is often beneficial, and that solving them simultaneously can be done efficiently. Our results suggest that self-supervised auxiliary tasks are effective data-dependent regularizers for representation learning. Our code is available at: \url{https://github.com/nathanielsimard/improving-fs-ssl}.

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