Strong Generalization
Strong generalization, the ability of machine learning models to perform well on unseen data, is a central objective in current research. Active areas of investigation include improving the robustness of self-supervised learning, understanding the optimization dynamics of transformers and other architectures (including CNNs and RNNs), and developing methods to enhance generalization through data augmentation, regularization techniques (e.g., logical regularization, consistency regularization), and improved training strategies (e.g., few-shot learning, meta-learning). These advancements are crucial for building reliable and adaptable AI systems across diverse applications, from image classification and natural language processing to healthcare and robotics.
Papers
On the Improvement of Generalization and Stability of Forward-Only Learning via Neural Polarization
Erik B. Terres-Escudero, Javier Del Ser, Pablo Garcia-Bringas
Linking Robustness and Generalization: A k* Distribution Analysis of Concept Clustering in Latent Space for Vision Models
Shashank Kotyan, Pin-Yu Chen, Danilo Vasconcellos Vargas
SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion Planning
Jianye Xu, Pan Hu, Bassam Alrifaee
Rethinking the Key Factors for the Generalization of Remote Sensing Stereo Matching Networks
Liting Jiang, Feng Wang, Wenyi Zhang, Peifeng Li, Hongjian You, Yuming Xiang