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
ALFA -- Leveraging All Levels of Feature Abstraction for Enhancing the Generalization of Histopathology Image Classification Across Unseen Hospitals
Milad Sikaroudi, Maryam Hosseini, Shahryar Rahnamayan, H. R. Tizhoosh
Bridging Trustworthiness and Open-World Learning: An Exploratory Neural Approach for Enhancing Interpretability, Generalization, and Robustness
Shide Du, Zihan Fang, Shiyang Lan, Yanchao Tan, Manuel Günther, Shiping Wang, Wenzhong Guo
Revealing the Underlying Patterns: Investigating Dataset Similarity, Performance, and Generalization
Akshit Achara, Ram Krishna Pandey
Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-Smoothing
Guoji Fu, Mohammed Haroon Dupty, Yanfei Dong, Lee Wee Sun
Improving Generalization in Visual Reinforcement Learning via Conflict-aware Gradient Agreement Augmentation
Siao Liu, Zhaoyu Chen, Yang Liu, Yuzheng Wang, Dingkang Yang, Zhile Zhao, Ziqing Zhou, Xie Yi, Wei Li, Wenqiang Zhang, Zhongxue Gan
Improving Generalization of Synthetically Trained Sonar Image Descriptors for Underwater Place Recognition
Ivano Donadi, Emilio Olivastri, Daniel Fusaro, Wanmeng Li, Daniele Evangelista, Alberto Pretto
Regularization, early-stopping and dreaming: a Hopfield-like setup to address generalization and overfitting
Elena Agliari, Francesco Alemanno, Miriam Aquaro, Alberto Fachechi
Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning
Kaijie Zhu, Jindong Wang, Xixu Hu, Xing Xie, Ge Yang