发表机构
Concordia University（康考迪亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出选择性反向传播（SBP），通过预分配参数子集限制梯度更新，在少样本类增量学习中实现高效适应，兼顾稳定性与低训练成本，并在跨域和长序列场景中保持强性能。
AI 中文摘要
少样本类增量学习（FSCIL）要求模型在严格的计算和内存约束下，从有限的样本中持续学习新类别，同时保留先前知识。现有方法面临一个困难的权衡：简单微调计算效率高，但遭受灾难性遗忘；基于重放的方法以大量计算和内存为代价缓解遗忘；无样本方法通常通过冻结大部分骨干网络来减少遗忘，以提高效率为代价牺牲适应性。我们提出选择性反向传播（SBP），一个确定性的参数预算框架，弥合了这一差距。SBP将梯度更新限制在预分配的网络参数子集上，冻结过去的知识并保留用于未来学习的无偏容量，实现快速适应而无需昂贵的掩码优化。我们表明，SBP在标准FSCIL基准上实现了强性能，同时训练时间接近朴素微调，且显著低于先前最先进方法。关键的是，我们的实验暴露了标准FSCIL评估的一个局限性：在短且分布一致的基准上的性能不一定能预测分布偏移或显著更长学习视野下的行为。因此，我们在跨域设置和80个会话的ImageNet-1K流上评估FSCIL方法。SBP在这些场景中保持强性能，同时维持低训练成本，提供了有利的稳定性-可塑性-效率权衡。我们的代码可在https URL获取。
英文摘要
Few-Shot Class-Incremental Learning (FSCIL) requires models to continuously learn new classes from limited samples while retaining prior knowledge, under strict constraints on compute and memory. Existing approaches lie along a difficult trade-off: simple fine-tuning is computationally efficient but suffers from catastrophic forgetting, replay-based methods mitigate forgetting at the cost of substantial compute and memory, and exemplar-free methods often reduce forgetting by freezing most of the backbone, improving efficiency at the expense of adaptability. We propose Selective Backpropagation (SBP), a deterministic parameter budgeting framework that bridges this gap. SBP restricts gradient updates to a pre-allocated subset of network parameters, freezing past knowledge and preserving unbiased capacity for future learning, enabling rapid adaptation without costly mask optimization. We show that SBP achieves strong performance across standard FSCIL benchmarks while requiring training time close to that of naive fine-tuning and substantially lower training time than prior SOTA methods. Crucially, our experiments expose a limitation of standard FSCIL evaluation: performance on short, distribution-consistent benchmarks does not necessarily predict behavior under distribution shift or over substantially longer learning horizons. We therefore evaluate FSCIL methods in cross-domain settings and over an 80-session ImageNet-1K stream. SBP remains strong across these regimes while maintaining low training cost, providing a favorable stability-plasticity-efficiency trade-off. Our code is available at https://github.com/PaInt-Lab/sbp-main-public.
