Clarity in chaos: Boosting few-shot classification through information suppression and sparsification
Xiaoxu Li, Luchen Ji, Rui Zhu, Zhanyu Ma, Jing‐Hao Xue · Pattern Recognition · 2025
The advance of deep learning has invigorated the research of few-shot classification. However, the interference of non-target information in feature representations hampers classification generalization. To tackle this issue, we propose an irrelevant information suppression (IIS) module, which is focused on suppressing the weight of unimportant information and elevating the sparsity of feature representations . An IIS network with three consecutive IIS modules is developed, to illustrate the progressive suppression of unimportant information and highlighting of key discriminative features of the target. Extensive experiments showcase the superior performance of our IIS network on five widely-used benchmark datasets. Furthermore, we show that the IIS module can be readily used as a plug-in module by state-of-the-art few-shot classifiers, and can clearly further improve their performance. Our code is available on GitHub at https://github.com/LC4188/IISNet . • We propose an IIS module to progressively suppress non-target information. • The IIS module can be readily used as a plug-in module. • The IIS module can clearly improve the performance of few-shot classifiers.