用数据驱动初始化分类器权重，让模型学新知识更快更稳。
A Good Start Matters: Enhancing Continual Learning with Data-Driven Weight Initialization
- 基于神经坍缩理论，用特征分布解析计算分类器初始权重。
- 相比随机初始化，初始损失下降超60%，适应新任务速度提升显著。
- 适合需要快速响应新数据的在线学习系统，如持续视觉识别。
为适应真实世界的数据流，持续学习（CL）系统需快速掌握新概念并保留旧知识。在向持续训练的深度神经网络（DNN）中添加新类别时，分类器权重通常随机初始化，导致初期训练损失急剧上升（损失尖峰）和训练不稳定，从而需要长时间训练才能达到最优收敛与精度，增加计算成本。受神经坍缩（NC）启发，我们提出一种权重初始化策略以提升持续学习效率。在使用均方误差训练的DNN中，神经坍缩产生最后一层的最小二乘（LS）分类器，其权重可由学习到的特征解析推导得出。我们利用该LS形式，以数据驱动方式初始化分类器权重，使其与特征分布对齐，而非随机初始化。该方法有效缓解初始损失尖峰，加速对新任务的适应。我们在大规模持续学习场景下评估该方法，结果表明其具备更快的适应速度和更高的持续学习性能。
原文摘要 · Abstract (English)
To adapt to real-world data streams, continual learning (CL) systems must rapidly learn new concepts while preserving and utilizing prior knowledge. When it comes to adding new information to continually-trained deep neural networks (DNNs), classifier weights for newly encountered categories are typically initialized randomly, leading to high initial training loss (spikes) and instability. Consequently, achieving optimal convergence and accuracy requires prolonged training, increasing computational costs. Inspired by Neural Collapse (NC), we propose a weight initialization strategy to improve learning efficiency in CL. In DNNs trained with mean-squared-error, NC gives rise to a Least-Square (LS) classifier in the last layer, whose weights can be analytically derived from learned features. We leverage this LS formulation to initialize classifier weights in a data-driven manner, aligning them with the feature distribution rather than using random initialization. Our method mitigates initial loss spikes and accelerates adaptation to new tasks. We evaluate our approach in large-scale CL settings, demonstrating faster adaptation and improved CL performance.
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