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Abstract
"Generalized Continual Category Discovery (GCCD) tackles learning from sequentially arriving, partially labeled datasets while uncovering new categories. Traditional methods depend on feature distillation to prevent forgetting the old knowledge. However, this strategy restricts the model’s ability to adapt and effectively distinguish new categories. To address this, we introduce a novel technique integrating a learnable projector with feature distillation, thus enhancing model adaptability without sacrificing past knowledge. The resulting distribution shift of the previously learned categories is mitigated with the auxiliary category adaptation network. We demonstrate that while each component offers modest benefits individually, their combination – dubbed CAMP (Category Adaptation Meets Projected distillation) – significantly improves the balance between learning new information and retaining old. CAMP exhibits superior performance across several GCCD and Class Incremental Learning scenarios. The code is available on Github."
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Authors (5)
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Grzegorz Rypeść
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Daniel Marczak
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Tomasz Trzciński dr hab. inż.
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Bartłomiej Twardowski dr inż.
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Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Rypeść G., Marczak D., Cygert S., Trzciński T., Twardowski B.: Category Adaptation Meets Projected Distillation in Generalized Continual Category Discovery// / : , 2024,
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-031-73247-8_19
- Sources of funding:
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- Spoza PG
- Verified by:
- Gdańsk University of Technology
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