Latent Separation

Latent separation focuses on disentangling underlying factors of variation within complex data, aiming to create more interpretable and efficient machine learning models. Current research emphasizes developing methods to achieve this disentanglement, particularly using generative models like GANs and VAEs, along with techniques such as contrastive learning and iterative learning to improve latent space organization and sample separation. This work is significant for enhancing model performance in various applications, including image generation, activity recognition, and few-shot learning, while also improving the interpretability of learned representations.