Papers › Clustered Task-Aware Meta-Learning by Learning from Learning Paths
Clustered Task-Aware Meta-Learning by Learning from Learning Paths
Danni Peng, Sinno Pan
To enable effective learning of new tasks with only few samples, meta-learning acquires common knowledge from the existing tasks with a globally shared meta-learner. To further address the problem of task heterogeneity, recent developments balance between customization and generalization by incorporating task clustering to generate the task-aware modulation to be applied on the global meta-learner. However, these methods learn task representation mostly from the features of input data, while the task-specific optimization process with respect to the base-learner model is often neglected. In this work, we propose a Clustered Task-Aware Meta-Learning (CTML) framework with task representation learned from its own learning path. We first conduct a rehearsed task learning from the common initialization, and collect a set of geometric quantities that adequately describes this learning path. By inputting this set of values into a meta path learner, we automatically abstract path representation optimized for the downstream clustering and modulation. To further save the computational cost incurred by the additional rehearsed learning, we devise a shortcut tunnel to directly map between the path and feature cluster assignments. Extensive experiments on two real-world application domains: few-shot image classification and cold-start recommendation demonstrate the superiority of CTML compared to state-of-the-art baselines.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections