Contrastive learning in self-supervised learning trains models to distinguish between similar and dissimilar data points without labeled data. The core idea is to create pairs of data samples: positive pairs (augmented views of the same input) and negative pairs (different inputs). The model learns by minimizing the similarity between negative pairs and maximizing it for positive pairs. For example, in image tasks, two randomly cropped or color-adjusted versions of the same photo form a positive pair, while images from different sources are negative pairs. The model generates embeddings where similar inputs cluster together, enabling it to capture meaningful patterns.
A typical architecture includes an encoder (e.g., a CNN or transformer) that maps inputs to embeddings and a projection head that maps embeddings to a lower-dimensional space for computing similarity. The loss function, like InfoNCE, measures how well the model distinguishes positives from negatives. For instance, given an anchor image, its augmented version (positive), and other images in a batch (negatives), the loss penalizes the model if the anchor is closer to negatives than the positive. Temperature scaling is often used to adjust how sharply the model focuses on hard negatives. This setup encourages the encoder to learn robust features invariant to noise or augmentations.
Contrastive learning is widely used in computer vision (e.g., SimCLR, MoCo) and NLP (e.g., sentence embeddings). In practice, developers benefit because it reduces reliance on labeled data. For instance, medical imaging can leverage unlabeled X-rays by contrasting different views of the same scan. The approach also scales well with batch size, as larger batches provide more negative samples. Challenges include selecting effective augmentations and managing computational costs. However, frameworks like PyTorch Lightning and TensorFlow simplify implementation, letting developers focus on tuning augmentations and model capacity for their domain.