LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures

Published: 16 Jan 2024, Last Modified: 12 Mar 2024ICLR 2024 spotlightEveryoneRevisionsBibTeX
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Keywords: Self Supervised Learning, Joint Embedding Architectures
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TL;DR: A SOTA method for predicting linear probing performance in joint embedding architectures
Abstract: Joint embedding (JE) architectures have emerged as a promising avenue for ac- quiring transferable data representations. A key obstacle to using JE methods, however, is the inherent challenge of evaluating learned representations without access to a downstream task, and an annotated dataset. Without efficient and re- liable evaluation, it is difficult to iterate on architectural and training choices for JE methods. In this paper, we introduce LiDAR (Linear Discriminant Analysis Rank), a metric designed to measure the quality of representations within JE archi- tectures. Our metric addresses several shortcomings of recent approaches based on feature covariance rank by discriminating between informative and uninforma- tive features. In essence, LiDAR quantifies the rank of the Linear Discriminant Analysis (LDA) matrix associated with the surrogate SSL task—a measure that intuitively captures the information content as it pertains to solving the SSL task. We empirically demonstrate that LiDAR significantly surpasses naive rank based approaches in its predictive power of optimal hyperparameters. Our proposed cri- terion presents a more robust and intuitive means of assessing the quality of rep- resentations within JE architectures, which we hope facilitates broader adoption of these powerful techniques in various domains.
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Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Submission Number: 8433