Summary of Versatile Incremental Learning: Towards Class and Domain-agnostic Incremental Learning, by Min-yeong Park et al.
Versatile Incremental Learning: Towards Class and Domain-Agnostic Incremental Learning
by Min-Yeong Park, Jae-Ho Lee, Gyeong-Moon Park
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, the authors propose a novel approach to incremental learning (IL) that addresses the challenge of accumulating knowledge from sequential input tasks while overcoming catastrophic forgetting. The proposed method, named Incremental Classifier with Adaptation Shift cONtrol (ICON), is designed to handle scenarios where the next task can be randomly altered, which poses intra-class domain confusion and inter-domain class confusion. To tackle this issue, the authors introduce a regularization method called Cluster-based Adaptation Shift conTrol (CAST) that controls the model to avoid confusion with previously learned knowledge. Additionally, they propose an Incremental Classifier (IC) that expands its output nodes to address the overwriting issue from different domains corresponding to a single class while maintaining previous knowledge. The authors demonstrate the effectiveness of their method on three benchmarks and provide implementation code at https://github.com/KHU-AGI/VIL. This work contributes to the development of more robust and adaptable IL models that can learn from diverse and dynamic data streams. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Incremental learning is a way for machines to get better at doing tasks over time, without forgetting what they learned before. Most current methods assume they know which type of task will come next, but this isn’t always the case. Researchers propose a new approach that can handle unexpected tasks and forget less easily. They use special techniques called Cluster-based Adaptation Shift conTrol (CAST) and Incremental Classifier to help the machine learn without interference from previous knowledge. The method is tested on three different datasets and shows promising results. |
Keywords
» Artificial intelligence » Regularization
