Learning New Concepts, Remembering the Old: Continual Learning for Multimodal Concept Bottleneck Models
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911091126173696 |
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| author | Lai, Songning Liao, Mingqian Hu, Zhangyi Yang, Jiayu Chen, Wenshuo Xiao, Hongru Tang, Jianheng Liao, Haicheng Yue, Yutao |
| author_facet | Lai, Songning Liao, Mingqian Hu, Zhangyi Yang, Jiayu Chen, Wenshuo Xiao, Hongru Tang, Jianheng Liao, Haicheng Yue, Yutao |
| contents | Concept Bottleneck Models (CBMs) enhance the interpretability of AI systems, particularly by bridging visual input with human-understandable concepts, effectively acting as a form of multimodal interpretability model. However, existing CBMs typically assume static datasets, which fundamentally limits their adaptability to real-world, continuously evolving multimodal data streams. To address this, we define a novel continual learning task for CBMs: simultaneously handling concept-incremental and class-incremental learning. This task requires models to continuously acquire new concepts (often representing cross-modal attributes) and classes while robustly preserving previously learned knowledge. To tackle this challenging problem, we propose CONceptual Continual Incremental Learning (CONCIL), a novel framework that fundamentally re-imagines concept and decision layer updates as linear regression problems. This reformulation eliminates the need for gradient-based optimization, thereby effectively preventing catastrophic forgetting. Crucially, CONCIL relies solely on recursive matrix operations, rendering it highly computationally efficient and well-suited for real-time and large-scale multimodal data applications. Experimental results compellingly demonstrate that CONCIL achieves "absolute knowledge memory" and significantly surpasses the performance of traditional CBM methods in both concept- and class-incremental settings, thus establishing a new paradigm for continual learning in CBMs, particularly valuable for dynamic multimodal understanding. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17471 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning New Concepts, Remembering the Old: Continual Learning for Multimodal Concept Bottleneck Models Lai, Songning Liao, Mingqian Hu, Zhangyi Yang, Jiayu Chen, Wenshuo Xiao, Hongru Tang, Jianheng Liao, Haicheng Yue, Yutao Machine Learning Cryptography and Security Computer Vision and Pattern Recognition Concept Bottleneck Models (CBMs) enhance the interpretability of AI systems, particularly by bridging visual input with human-understandable concepts, effectively acting as a form of multimodal interpretability model. However, existing CBMs typically assume static datasets, which fundamentally limits their adaptability to real-world, continuously evolving multimodal data streams. To address this, we define a novel continual learning task for CBMs: simultaneously handling concept-incremental and class-incremental learning. This task requires models to continuously acquire new concepts (often representing cross-modal attributes) and classes while robustly preserving previously learned knowledge. To tackle this challenging problem, we propose CONceptual Continual Incremental Learning (CONCIL), a novel framework that fundamentally re-imagines concept and decision layer updates as linear regression problems. This reformulation eliminates the need for gradient-based optimization, thereby effectively preventing catastrophic forgetting. Crucially, CONCIL relies solely on recursive matrix operations, rendering it highly computationally efficient and well-suited for real-time and large-scale multimodal data applications. Experimental results compellingly demonstrate that CONCIL achieves "absolute knowledge memory" and significantly surpasses the performance of traditional CBM methods in both concept- and class-incremental settings, thus establishing a new paradigm for continual learning in CBMs, particularly valuable for dynamic multimodal understanding. |
| title | Learning New Concepts, Remembering the Old: Continual Learning for Multimodal Concept Bottleneck Models |
| topic | Machine Learning Cryptography and Security Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.17471 |