Rethinking Evaluation Protocols of Visual Representations Learned via
Self-supervised Learning
- URL: http://arxiv.org/abs/2304.03456v1
- Date: Fri, 7 Apr 2023 03:03:19 GMT
- Title: Rethinking Evaluation Protocols of Visual Representations Learned via
Self-supervised Learning
- Authors: Jae-Hun Lee, Doyoung Yoon, ByeongMoon Ji, Kyungyul Kim, Sangheum Hwang
- Abstract summary: Self-supervised learning (SSL) is used to evaluate the quality of visual representations learned via self-supervised learning (SSL)
Existing SSL methods have shown good performances under those evaluation protocols.
We try to figure out the cause of performance sensitivity by conducting extensive experiments with state-of-the-art SSL methods.
- Score: 1.0499611180329804
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Linear probing (LP) (and $k$-NN) on the upstream dataset with labels (e.g.,
ImageNet) and transfer learning (TL) to various downstream datasets are
commonly employed to evaluate the quality of visual representations learned via
self-supervised learning (SSL). Although existing SSL methods have shown good
performances under those evaluation protocols, we observe that the performances
are very sensitive to the hyperparameters involved in LP and TL. We argue that
this is an undesirable behavior since truly generic representations should be
easily adapted to any other visual recognition task, i.e., the learned
representations should be robust to the settings of LP and TL hyperparameters.
In this work, we try to figure out the cause of performance sensitivity by
conducting extensive experiments with state-of-the-art SSL methods. First, we
find that input normalization for LP is crucial to eliminate performance
variations according to the hyperparameters. Specifically, batch normalization
before feeding inputs to a linear classifier considerably improves the
stability of evaluation, and also resolves inconsistency of $k$-NN and LP
metrics. Second, for TL, we demonstrate that a weight decay parameter in SSL
significantly affects the transferability of learned representations, which
cannot be identified by LP or $k$-NN evaluations on the upstream dataset. We
believe that the findings of this study will be beneficial for the community by
drawing attention to the shortcomings in the current SSL evaluation schemes and
underscoring the need to reconsider them.
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