Delve into Base-Novel Confusion: Redundancy Exploration for Few-Shot Class-Incremental Learning
CoRR(2024)
Abstract
Few-shot class-incremental learning (FSCIL) aims to acquire knowledge from
novel classes with limited samples while retaining information about base
classes. Existing methods address catastrophic forgetting and overfitting by
freezing the feature extractor during novel-class learning. However, these
methods usually tend to cause the confusion between base and novel classes,
i.e., classifying novel-class samples into base classes. In this paper, we
delve into this phenomenon to study its cause and solution. We first interpret
the confusion as the collision between the novel-class and the base-class
region in the feature space. Then, we find the collision is caused by the
label-irrelevant redundancies within the base-class feature and pixel space.
Through qualitative and quantitative experiments, we identify this redundancy
as the shortcut in the base-class training, which can be decoupled to alleviate
the collision. Based on this analysis, to alleviate the collision between base
and novel classes, we propose a method for FSCIL named Redundancy Decoupling
and Integration (RDI). RDI first decouples redundancies from base-class space
to shrink the intra-base-class feature space. Then, it integrates the
redundancies as a dummy class to enlarge the inter-base-class feature space.
This process effectively compresses the base-class feature space, creating
buffer space for novel classes and alleviating the model's confusion between
the base and novel classes. Extensive experiments across benchmark datasets,
including CIFAR-100, miniImageNet, and CUB-200-2011 demonstrate that our method
achieves state-of-the-art performance.
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