用CLIP动态融合参数，缓解增量学习中的遗忘问题。
Adaptive Weighted Parameter Fusion with CLIP for Class-Incremental Learning
- 基于CLIP设计自适应加权参数融合机制
- 在多个基准上实现优于现有方法的分类性能
- 适合需要持续学习新类别的视觉模型应用
类别增量学习（CIL）使模型能逐步吸收新类别知识，并对所有已见类别构建通用分类器。但在优化新类别时，旧类别知识不可避免地被抹除，导致灾难性遗忘。解决此问题需在保留旧知识与容纳新信息之间权衡，但这一过程常导致部分判别能力损失。为此，本文提出基于对比语言-图像预训练（CLIP）的自适应加权参数融合方法，不仅考虑不同任务间数据分布的差异性，还最大限度保留参数矩阵的有效信息。此外，引入平衡因子以协调相邻任务间的分布对齐与可区分性。在多个传统基准上的实验验证了所提方法的优越性。
原文摘要 · Abstract (English)
Class-incremental Learning (CIL) enables the model to incrementally absorb knowledge from new classes and build a generic classifier across all previously encountered classes. When the model optimizes with new classes, the knowledge of previous classes is inevitably erased, leading to catastrophic forgetting. Addressing this challenge requires making a trade-off between retaining old knowledge and accommodating new information. However, this balancing process often requires sacrificing some information, which can lead to a partial loss in the model's ability to discriminate between classes. To tackle this issue, we design the adaptive weighted parameter fusion with Contrastive Language-Image Pre-training (CLIP), which not only takes into account the variability of the data distribution of different tasks, but also retains all the effective information of the parameter matrix to the greatest extent. In addition, we introduce a balance factor that can balance the data distribution alignment and distinguishability of adjacent tasks. Experimental results on several traditional benchmarks validate the superiority of the proposed method.
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