Search Results for author:
Found 3 papers, 2 papers with code
Learning Equi-angular Representations for Online Continual Learning
Online continual learning suffers from an underfitted solution due to insufficient training for prompt model update (e. g., single-epoch training).
Just Say the Name: Online Continual Learning with Category Names Only via Data Generation
In real-world scenarios, extensive manual annotation for continual learning is impractical due to prohibitive costs.
Online Continual Learning For Interactive Instruction Following Agents
To take a step towards a more realistic embodied agent learning scenario, we propose two continual learning setups for embodied agents; learning new behaviors (Behavior Incremental Learning, Behavior-IL) and new environments (Environment Incremental Learning, Environment-IL) For the tasks, previous 'data prior' based continual learning methods maintain logits for the past tasks.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
