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.

FeTrIL 框架的研究扩展至新颖的实验领域，通过对多个具有挑战性的数据集和增量设置进行各种过采样技术和动态优化策略的实证研究来研究其有效性，特别是探索过采样对特征可用性的影响以及不同的优化方法对增量学习结果的影响。通过在 CIFAR100、Tiny-ImageNet 和 ImageNet-Subset 上进行全面实验，FeTrIL 在新旧类别的准确性平衡方面表现出卓越性能，相对于其他十种方法，其优越性能得到了肯定。研究揭示了过采样和优化对于类别增量学习的微妙影响，为特征空间操作在课堂增量学习中的更精细理解做出了贡献。本文的扩展研究为更适应和高效的 EFCIL 方法铺平了道路，承诺在不需要样本的情况下显著改善处理灾难性遗忘。