Contrastive Factor Analysis

Contrastive Factor Analysis (CFA) combines the strengths of factor analysis—a statistical method for dimensionality reduction and uncovering latent structures—with contrastive learning, a powerful technique for unsupervised representation learning. Current research focuses on developing CFA models that improve expressiveness, robustness, and interpretability, often incorporating non-negativity constraints for disentangled representations and employing variations of contrastive loss functions, such as symmetrical or asymmetrical approaches, to enhance performance. These advancements are impacting various fields, including relation extraction, 3D object segmentation, and activity recognition, by enabling more effective handling of high-dimensional data, incomplete information, and few-shot learning scenarios.