A surprisingly simple technique to control the pretraining bias for better transfer: Expand or Narrow your representation

• URL: http://arxiv.org/abs/2304.05369v1
• Date: Tue, 11 Apr 2023 17:24:29 GMT
• Title: A surprisingly simple technique to control the pretraining bias for better transfer: Expand or Narrow your representation
• Authors: Florian Bordes, Samuel Lavoie, Randall Balestriero, Nicolas Ballas, Pascal Vincent
• Abstract summary: Self-Supervised Learning (SSL) models rely on a pretext task to learn representations. We show that merely changing its dimensionality -- by changing only the size of the backbone's very last block -- is a remarkably effective technique to mitigate the pretraining bias.
• Score: 22.866948071297767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-Supervised Learning (SSL) models rely on a pretext task to learn representations. Because this pretext task differs from the downstream tasks used to evaluate the performance of these models, there is an inherent misalignment or pretraining bias. A commonly used trick in SSL, shown to make deep networks more robust to such bias, is the addition of a small projector (usually a 2 or 3 layer multi-layer perceptron) on top of a backbone network during training. In contrast to previous work that studied the impact of the projector architecture, we here focus on a simpler, yet overlooked lever to control the information in the backbone representation. We show that merely changing its dimensionality -- by changing only the size of the backbone's very last block -- is a remarkably effective technique to mitigate the pretraining bias. It significantly improves downstream transfer performance for both Self-Supervised and Supervised pretrained models.

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