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<p>Few-Shot Learning (FSL), which involves learning to generalize using only a few data samples, has demonstrated promising and superior performances to ordinary CNN methods. While Bayesian-based estimation approaches using Kullback-Leibler (KL) divergence have shown improvements, they remain vulnerable to adversarial attacks and natural noises. We introduce ANROT-HELANet, an Adversarially and Naturally RObusT Hellinger Aggregation Network that significantly advances the state-of-the-art in FSL robustness and performance. Our approach implements an adversarially and naturally robust Hellinger distance-based feature class aggregation scheme, demonstrating resilience to adversarial perturbations up to <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\epsilon = 0.30\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>ϵ</mi> <mo>=</mo> <mn>0.30</mn> </mrow> </math></EquationSource> </InlineEquation> and Gaussian noise up to <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\sigma =0.30\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>σ</mi> <mo>=</mo> <mn>0.30</mn> </mrow> </math></EquationSource> </InlineEquation>. The network achieves substantial improvements across benchmark datasets, including gains of 1.20% and 1.40% for 1-shot and 5-shot scenarios on miniImageNet respectively. We introduce a novel Hellinger Similarity contrastive loss function that generalizes cosine similarity contrastive loss for variational few-shot inference scenarios. Our approach also achieves superior image reconstruction quality with a FID score of 2.75, outperforming traditional VAE (3.43) and WAE (3.38) approaches. Extensive experiments conducted on four few-shot benchmarked datasets verify that ANROT-HELANet’s combination of Hellinger distance-based feature aggregation, attention mechanisms, and our novel loss function establishes new state-of-the-art performance while maintaining robustness against both adversarial and natural perturbations.</p>

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ANROT-HELANet: adverserially and naturally robust attention-based aggregation network via the hellinger distance for few-shot classification

  • 期刊论文

  • 发表日期：

    2026年1月27日

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  • Gao Yu Lee,
  • Tanmoy Dam,
  • Md Meftahul Ferdaus,
  • Daniel Puiu Poenar,
  • Vu N. Duong

摘要

Few-Shot Learning (FSL), which involves learning to generalize using only a few data samples, has demonstrated promising and superior performances to ordinary CNN methods. While Bayesian-based estimation approaches using Kullback-Leibler (KL) divergence have shown improvements, they remain vulnerable to adversarial attacks and natural noises. We introduce ANROT-HELANet, an Adversarially and Naturally RObusT Hellinger Aggregation Network that significantly advances the state-of-the-art in FSL robustness and performance. Our approach implements an adversarially and naturally robust Hellinger distance-based feature class aggregation scheme, demonstrating resilience to adversarial perturbations up to \(\epsilon = 0.30\) ϵ = 0.30 and Gaussian noise up to \(\sigma =0.30\) σ = 0.30 . The network achieves substantial improvements across benchmark datasets, including gains of 1.20% and 1.40% for 1-shot and 5-shot scenarios on miniImageNet respectively. We introduce a novel Hellinger Similarity contrastive loss function that generalizes cosine similarity contrastive loss for variational few-shot inference scenarios. Our approach also achieves superior image reconstruction quality with a FID score of 2.75, outperforming traditional VAE (3.43) and WAE (3.38) approaches. Extensive experiments conducted on four few-shot benchmarked datasets verify that ANROT-HELANet’s combination of Hellinger distance-based feature aggregation, attention mechanisms, and our novel loss function establishes new state-of-the-art performance while maintaining robustness against both adversarial and natural perturbations.