Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning

• URL: http://arxiv.org/abs/2402.14789v1
• Date: Thu, 22 Feb 2024 18:46:22 GMT
• Title: Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning
• Authors: Johnathan Xie, Yoonho Lee, Annie S. Chen, Chelsea Finn
• Abstract summary: We present Self-guided Masked Autoencoders (SMA), a fully domain-agnostic masked modeling method. SMA trains an attention based model using a masked modeling objective, by learning masks to sample without any domain-specific assumptions. We evaluate SMA on three self-supervised learning benchmarks in protein biology, chemical property prediction, and particle physics.
• Score: 58.93724285214628
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervised learning excels in learning representations from large amounts of unlabeled data, demonstrating success across multiple data modalities. Yet, extending self-supervised learning to new modalities is non-trivial because the specifics of existing methods are tailored to each domain, such as domain-specific augmentations which reflect the invariances in the target task. While masked modeling is promising as a domain-agnostic framework for self-supervised learning because it does not rely on input augmentations, its mask sampling procedure remains domain-specific. We present Self-guided Masked Autoencoders (SMA), a fully domain-agnostic masked modeling method. SMA trains an attention based model using a masked modeling objective, by learning masks to sample without any domain-specific assumptions. We evaluate SMA on three self-supervised learning benchmarks in protein biology, chemical property prediction, and particle physics. We find SMA is capable of learning representations without domain-specific knowledge and achieves state-of-the-art performance on these three benchmarks.

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