Latent Subspace

Latent subspace learning aims to discover lower-dimensional representations of high-dimensional data, revealing underlying structure and improving model interpretability and generalization. Current research focuses on developing novel algorithms, such as variational autoencoders (VAEs) and contrastive learning methods, to disentangle these subspaces, enabling the identification of meaningful factors of variation and mitigating issues like shortcut learning and dimensional collapse. This work has significant implications for diverse fields, including image analysis, speech processing, and multi-task learning, by enhancing model explainability, improving performance on complex datasets, and facilitating robust cross-domain adaptation.