Contrastive learning is a machine learning approach that trains a model to distinguish related examples from unrelated ones. It typically brings representations of similar items closer together and pushes representations of dissimilar items farther apart. “Multiple views” may mean different versions or features of the same item, such as text and citations describing a legal case.
In cyber security, contrastive learning can help systems recognize patterns in data, including suspicious activity, even when labeled examples are limited. For example, a model might learn that different observations of the same benign event are alike while separating them from indicators of an attack. In legal search, the method could help compare cases or opinions, but the result depends on how examples and similarity are defined. Poor choices can overemphasize common patterns and obscure less frequent but important material, so performance should be evaluated for coverage as well as accuracy.