通过可学习漂移补偿实现无样本的持续表征学习
Exemplar-free Continual Representation Learning via Learnable Drift Compensation
AI总结：
针对无样本类别增量持续表征学习中的原型语义漂移问题，提出可学习漂移补偿方法LDC，可适配有监督/无监督骨干网络，还能结合自监督方法实现首个无样本半监督持续学习，在多数据集上达SOTA。
摘要：
使用从头训练的骨干网络、且首个任务规模较小的无样本类别增量学习，对持续表征学习提出了重大挑战。基于原型的方法在持续更新时会面临语义漂移的关键问题，即旧类别原型会在新特征空间中漂移到不同位置。通过对基于原型的持续学习的分析，我们表明遗忘并非由特征提取器的判别能力下降导致，且有可能通过漂移补偿得到修正。为解决这一问题，我们提出了可学习漂移补偿（Learnable Drift Compensation，LDC），它能有效缓解任意动态骨干网络中的漂移问题，无论该网络是有监督还是无监督的。LDC 速度快，且易于集成到现有持续学习方法之上。此外，我们展示了 LDC 如何与自监督持续学习方法结合应用，由此诞生了首个无样本的半监督持续学习方法。我们在多个数据集的有监督和半监督设置下均取得了当前最优性能。代码可在 https://github.com/alviur/ldc 获取。
英文摘要：
Exemplar-free class-incremental learning using a backbone trained from scratch and starting from a small first task presents a significant challenge for continual representation learning. Prototype-based approaches, when continually updated, face the critical issue of semantic drift due to which the old class prototypes drift to different positions in the new feature space. Through an analysis of prototype-based continual learning, we show that forgetting is not due to diminished discriminative power of the feature extractor, and can potentially be corrected by drift compensation. To address this, we propose Learnable Drift Compensation (LDC), which can effectively mitigate drift in any moving backbone, whether supervised or unsupervised. LDC is fast and straightforward to integrate on top of existing continual learning approaches. Furthermore, we showcase how LDC can be applied in combination with self-supervised CL methods, resulting in the first exemplar-free semi-supervised continual learning approach. We achieve state-of-the-art performance in both supervised and semi-supervised settings across multiple datasets. Code is available at \url{https://github.com/alviur/ldc}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
