Domain Expansion

Domain expansion in machine learning focuses on adapting models to handle data from new, unseen domains without extensive retraining. Current research emphasizes techniques like pseudo-label filtering for continual test-time adaptation, latent domain expansion to enhance feature representation, and gradual source domain expansion to mitigate error propagation during unsupervised adaptation. These advancements aim to improve the generalization capabilities of models, leading to more robust and adaptable systems across diverse real-world applications, such as person re-identification and hyperspectral image classification.