提出简单高效方法，让模型持续学习不遗忘且能灵活适应新任务。
A Simple Baseline for Stable and Plastic Neural Networks
- 用轻量激活修改和梯度调度，平衡模型稳定与灵活。
- 在Continual ImageNet上性能超越或媲美顶尖方法，计算开销更低。
- 适合需要长期学习的视觉应用，也可作新方法对比基准。
计算机视觉中的持续学习要求模型在不断接收新任务时保持对旧知识的记忆，但现有方法往往过度偏向灵活性或稳定性。本文提出RDBP，一种简单、低开销的基线方法，融合两种互补机制：ReLUDown通过轻量激活调整，保持特征敏感性并防止神经元失活；Decreasing Backpropagation采用生物启发的梯度调度策略，逐步减少早期层的梯度更新，避免灾难性遗忘。在Continual ImageNet基准测试中，RDBP在保持高性能的同时，显著降低计算成本，其表现匹配或超过当前最优方法。该工作不仅为真实场景下的持续学习提供实用方案，也为未来研究提供了清晰的评估基准。
原文摘要 · Abstract (English)
Continual learning in computer vision requires that models adapt to a continuous stream of tasks without forgetting prior knowledge, yet existing approaches often tip the balance heavily toward either plasticity or stability. We introduce RDBP, a simple, low-overhead baseline that unites two complementary mechanisms: ReLUDown, a lightweight activation modification that preserves feature sensitivity while preventing neuron dormancy, and Decreasing Backpropagation, a biologically inspired gradient-scheduling scheme that progressively shields early layers from catastrophic updates. Evaluated on the Continual ImageNet benchmark, RDBP matches or exceeds the plasticity and stability of state-of-the-art methods while reducing computational cost. RDBP thus provides both a practical solution for real-world continual learning and a clear benchmark against which future continual learning strategies can be measured.
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