8 papers
OpenWorldLib: A Unified Codebase and Definition of Advanced World Models
DataFlow Team, Bohan Zeng, Daili Hua +39
World models have garnered significant attention as a promising research direction in artificial intelligence, yet a clear and unified definition remains lacking. In this paper, we…
AffineLens: Capturing the Continuous Piecewise Affine Functions of Neural Networks
Yi Wei, Xuan Qi, Furao Shen +3
Piecewise affine neural networks (PANNs) provide a principled geometric perspective on neural network expressivity by characterizing the input--output map as a continuous piecewise…
Free-Flow Class-Incremental Learning: Towards Robust CIL under Variable Class Arrivals
Zhiming Xu, Baile Xu, Jian Zhao +2
Class-incremental learning (CIL) is commonly evaluated under predefined schedules with fixed or nearly equal class increments, leaving irregular class-arrival scenarios underexplor…
Dual Prototypes for Adaptive Pre-Trained Model in Class-Incremental Learning
Zhiming Xu, Suorong Yang, Baile Xu +2
Class-incremental learning (CIL) aims to learn new classes while retaining previous knowledge. Although pre-trained model (PTM) based approaches show strong performance, directly f…
Multiple Queries with Multiple Keys: A Precise Prompt Matching Paradigm for Prompt-based Continual Learning
Dunwei Tu, Huiyu Yi, Yuchi Wang +3
Continual learning requires machine learning models to continuously acquire new knowledge in dynamic environments while avoiding the forgetting of previous knowledge. Prompt-based…
Embedding Space Allocation with Angle-Norm Joint Classifiers for Few-Shot Class-Incremental Learning
Dunwei Tu, Huiyu Yi, Tieyi Zhang +3
Few-shot class-incremental learning (FSCIL) aims to continually learn new classes from only a few samples without forgetting previous ones, requiring intelligent agents to adapt to…