破解可塑性瓶颈，清华团队新作刷榜持续学习：可迁移任务关系指导训练

Core Insights - Tsinghua University's research team has proposed a novel continual learning (CL) framework called H-embedding guided hypernetwork, which addresses the issue of catastrophic forgetting in AI models by focusing on task relationships [1][4][21] - The framework aims to enhance the model's ability to absorb new knowledge while maintaining performance on old tasks, thus facilitating long-term intelligence in AI systems [1][21] Group 1: Problem Identification - Catastrophic forgetting is a significant bottleneck in the practical application of continual learning, where models forget old knowledge when learning new tasks [1][4] - Existing CL methods primarily adopt a model-centric approach, neglecting the intrinsic relationships between tasks, which directly influence knowledge transfer efficiency [1][8] Group 2: Proposed Solution - The H-embedding guided hypernetwork framework introduces a task-relation-centric approach, constructing transferable task embeddings (H-embedding) before learning new tasks [4][6] - This method allows for explicit encoding of task relationships in the CL process, enabling the model to manage knowledge transfer more effectively [6][21] Group 3: Methodology - H-embedding is derived from H-score, which quantifies the transfer value from old tasks to current tasks, facilitating efficient computation of transferability [9][11] - The framework employs a hypernetwork to generate task-specific parameters based on the H-embedding, allowing for automatic adjustment of parameters according to task differences [12][17] Group 4: Experimental Results - The proposed framework has shown superior performance across multiple CL benchmarks, including CIFAR-100, ImageNet-R, and DomainNet, demonstrating its robustness and scalability [18][20] - The model exhibits strong forward and backward transfer capabilities, with minimal interference from new tasks on old tasks, and effectively absorbs knowledge from previous tasks [20] Group 5: Future Directions - The research indicates potential applications of task-structure-aware methods in cross-modal incremental learning, long-term task adaptation for large models, and automated learning sequence planning [21][23] - This approach aims to contribute to the development of more scalable and adaptable general AI systems [21]