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

Key Points

• This model achieves significant advancements in few-shot classification, outperforming traditional approaches.
• Key evidence shows that using the Hellinger distance results in a 15% increase in classification accuracy on challenging datasets.
• The method involves an attention-based aggregation network evaluated through experimental trials on various benchmarks.
• Findings suggest that incorporating adversarial robustness enhances the model's effectiveness across diverse tasks.