Consistent Representation

Consistent representation learning aims to generate feature representations that remain stable and comparable across different models, datasets, or time points, enabling seamless integration of new data or model updates without requiring extensive retraining or reprocessing. Current research focuses on developing methods to achieve this consistency, including contrastive learning, graph-based regularization, and techniques leveraging fixed classifiers or manifold alignment, often within the context of multi-view learning or continual learning scenarios. This work is significant for improving the efficiency and robustness of machine learning systems in various applications, such as search and retrieval, action quality assessment, and few-shot learning, by reducing computational costs and mitigating catastrophic forgetting.