GPT-4: Guillotine Regularization (GR) is a critical technique in Self-Supervised Learning (SSL) that significantly improves generalization performance in transfer learning scenarios. This study investigates the reasons behind GR’s success and challenges the idea of discarding the entire projector in SSL. It reveals that the optimal layer to use may vary depending on the training setup, data, or downstream task, and provides insights on reducing the need for a projector by aligning the pretext SSL task with the downstream task.
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