Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models

• URL: http://arxiv.org/abs/2403.07066v1
• Date: Mon, 11 Mar 2024 18:00:47 GMT
• Title: Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models
• Authors: Philip Harris, Michael Kagan, Jeffrey Krupa, Benedikt Maier, Nathaniel Woodward
• Abstract summary: Self-Supervised Learning (SSL) is at the core of training modern large machine learning models. We propose RS3L, a novel simulation-based SSL strategy that employs a method of re-simulation to drive data augmentation. In addition to our results, we make the RS3L dataset publicly available for further studies on how to improve SSL strategies.
• Score: 1.230412738960606
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-Supervised Learning (SSL) is at the core of training modern large machine learning models, providing a scheme for learning powerful representations that can be used in a variety of downstream tasks. However, SSL strategies must be adapted to the type of training data and downstream tasks required. We propose RS3L, a novel simulation-based SSL strategy that employs a method of re-simulation to drive data augmentation for contrastive learning. By intervening in the middle of the simulation process and re-running simulation components downstream of the intervention, we generate multiple realizations of an event, thus producing a set of augmentations covering all physics-driven variations available in the simulator. Using experiments from high-energy physics, we explore how this strategy may enable the development of a foundation model; we show how R3SL pre-training enables powerful performance in downstream tasks such as discrimination of a variety of objects and uncertainty mitigation. In addition to our results, we make the RS3L dataset publicly available for further studies on how to improve SSL strategies.

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