  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.
