Self-supervised learning, or SSL, holds the key to expanding the usage of machine learning in real-world tasks by alleviating heavy human supervision. Contrastive learning and its varieties have been SSL strategies in various fields. We use margins as a stepping stone for understanding how contrastive learning works at a deeper level and providing potential directions to improve representation learning. Through gradient analysis, we found that margins scale gradients in three different ways: emphasizing positive samples, de-emphasizing positive samples when angles of positive samples are wide, and attenuating the diminishing gradients as the estimated probability approaches the target probability. We separately analyze each and provide possible directions for improving SSL frameworks. Our experimental results demonstrate that these properties can contribute to acquiring better representations, which can enhance performance in both seen and unseen datasets.

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