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Title: Incorporating simulated spatial context information improves the effectiveness of contrastive learning models
Visual learning often occurs in a specific context, where an agent acquires skills through exploration and tracking of its location in a consistent environment. The historical spatial context of the agent provides a similarity signal for self-supervised contrastive learning. We present a unique approach, termed Environmental Spatial Similarity (ESS), that complements existing contrastive learning methods. Using images from simulated, photorealistic environments as an experimental setting, we demonstrate that ESS outperforms traditional instance discrimination approaches. Moreover, sampling additional data from the same environment substantially improves accuracy and provides new augmentations. ESS allows remarkable proficiency in room classification and spatial prediction tasks, especially in unfamiliar environments. This learning paradigm has the potential to enable rapid visual learning in agents operating in new environments with unique visual characteristics. Potentially transformative applications span from robotics to space exploration. Our proof of concept demonstrates improved efficiency over methods that rely on extensive, disconnected datasets.  more » « less
Award ID(s):
2216127
PAR ID:
10628467
Author(s) / Creator(s):
; ; ;
Editor(s):
Wang, Wanying
Publisher / Repository:
Cell Press
Date Published:
Journal Name:
Patterns
Volume:
5
Issue:
5
ISSN:
2666-3899
Page Range / eLocation ID:
100964
Subject(s) / Keyword(s):
Contrastive learning Representation learning Virtual environment Developmental psychology Deep learning Bio-inspired computing Intelligent agent
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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