Summary of Adaptive Discovering and Merging For Incremental Novel Class Discovery, by Guangyao Chen et al.
Adaptive Discovering and Merging for Incremental Novel Class Discovery
by Guangyao Chen, Peixi Peng, Yangru Huang, Mengyue Geng, Yonghong Tian
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The proposed Adaptive Discovering and Merging (ADM) paradigm is a novel approach to discovering new classes in unlabeled data while mitigating catastrophic forgetting of established knowledge. This method decouples representation learning from novel class discovery, using Triple Comparison (TC) and Probability Regularization (PR) to constrain probability discrepancies and diversity for adaptive category assignment. The ADM framework also includes an Adaptive Model Merging (AMM) structure with base and novel branches, reducing interference between the two while preserving previous knowledge and avoiding performance loss or parameter growth. Experimental results on several datasets demonstrate that ADM outperforms existing class-incremental Novel Class Discovery (class-iNCD) approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Adapting to new information is crucial for lifelong learning. A big challenge is figuring out what’s new and what we already know, while making sure we don’t forget important details. To solve this problem, researchers introduced a new way of discovering new categories called Adaptive Discovering and Merging (ADM). This approach breaks down the process into two parts: finding new categories and combining that knowledge with what we already know. By doing it in a special way, ADM ensures we don’t lose important information while learning something new. |
Keywords
» Artificial intelligence » Probability » Regularization » Representation learning
