FeTrIL++: 结合爬山法的无样本类别增量学习特征翻译方法
FeTrIL++: Feature Translation for Exemplar-Free Class-Incremental Learning with Hill-Climbing
AI总结：
本研究针对无样本类别增量学习的灾难性遗忘问题，在FeTrIL框架基础上拓展过采样与动态优化策略研究，经多数据集实验验证其在平衡新旧类别准确率上的优势，为更高效的无样本增量学习方法提供支撑。
摘要：
无样本类别增量学习（EFCIL）面临重大挑战，主要源于灾难性遗忘问题，需要在稳定性与可塑性之间实现精妙平衡，才能准确识别新类别和已有类别。传统EFCIL方法通常存在偏向性：要么通过持续微调侧重模型可塑性，要么在初始增量阶段后采用固定特征提取器侧重稳定性。本研究在FeTrIL基础框架之上，将研究拓展至新的实验领域，在多个高难度数据集和增量设置下检验各类过采样技术与动态优化策略的效果。我们具体探究了过采样如何相对于特征可用情况影响准确率，以及包括动态重校准、特征池多样化在内的不同优化方法如何影响增量学习结果。在CIFAR100、Tiny-ImageNet和ImageNet-Subset上开展的全面实验结果表明，与10种现有方法相比，FeTrIL在平衡新类别与旧类别准确率方面表现更优。值得注意的是，我们的拓展研究揭示了过采样与优化对EFCIL的细微影响，有助于更精准地理解类别增量学习中的特征空间操作。FeTrIL及其在本文中的拓展分析FeTrIL++为更具适应性、更高效的EFCIL方法铺平了道路，有望在无需样本的情况下大幅提升灾难性遗忘问题的处理效果。
英文摘要：
Exemplar-free class-incremental learning (EFCIL) poses significant challenges, primarily due to catastrophic forgetting, necessitating a delicate balance between stability and plasticity to accurately recognize both new and previous classes. Traditional EFCIL approaches typically skew towards either model plasticity through successive fine-tuning or stability by employing a fixed feature extractor beyond the initial incremental state. Building upon the foundational FeTrIL framework, our research extends into novel experimental domains to examine the efficacy of various oversampling techniques and dynamic optimization strategies across multiple challenging datasets and incremental settings. We specifically explore how oversampling impacts accuracy relative to feature availability and how different optimization methodologies, including dynamic recalibration and feature pool diversification, influence incremental learning outcomes. The results from these comprehensive experiments, conducted on CIFAR100, Tiny-ImageNet, and an ImageNet-Subset, under-score the superior performance of FeTrIL in balancing accuracy for both new and past classes against ten contemporary methods. Notably, our extensions reveal the nuanced impacts of oversampling and optimization on EFCIL, contributing to a more refined understanding of feature-space manipulation for class incremental learning. FeTrIL and its extended analysis in this paper FeTrIL++ pave the way for more adaptable and efficient EFCIL methodologies, promising significant improvements in handling catastrophic forgetting without the need for exemplars.
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。
