Tag
contrastive-learning
- Sep 15, 2026 InfoNCE and the Temperature Parameter How the contrastive objective turns similarity scores into a softmax over the batch, why the temperature controls effective negative count and gradient shape, and what breaks when it is set wrong. deep learning
- Aug 17, 2026 Data Augmentation as an Invariance Specification Augmentations declare which transformations should not change a representation; the choice fixes what the embedding can ignore, and a mismatched set teaches the model to discard signal it needs later. deep learning
- Mar 13, 2026 Alignment and Uniformity as Separate Objectives Contrastive losses can be decomposed into a term that pulls positive pairs together and a term that spreads all representations apart, and the two terms trade against each other in measurable ways. deep learning
- Aug 23, 2024 False Negatives and Batch Composition Every off-diagonal entry in a contrastive batch is treated as a negative, including entries that are semantically identical; the resulting pressure shapes the space and is controlled by what goes into the batch. deep learning