Robust Few-shot Learning Without Using any Adversarial Samples

• URL: http://arxiv.org/abs/2211.01598v1
• Date: Thu, 3 Nov 2022 05:58:26 GMT
• Title: Robust Few-shot Learning Without Using any Adversarial Samples
• Authors: Gaurav Kumar Nayak, Ruchit Rawal, Inder Khatri, Anirban Chakraborty
• Abstract summary: A few efforts have been made to combine the few-shot problem with the robustness objective using sophisticated Meta-Learning techniques. We propose a simple but effective alternative that does not require any adversarial samples. Inspired by the cognitive decision-making process in humans, we enforce high-level feature matching between the base class data and their corresponding low-frequency samples.
• Score: 19.34427461937382
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The high cost of acquiring and annotating samples has made the `few-shot' learning problem of prime importance. Existing works mainly focus on improving performance on clean data and overlook robustness concerns on the data perturbed with adversarial noise. Recently, a few efforts have been made to combine the few-shot problem with the robustness objective using sophisticated Meta-Learning techniques. These methods rely on the generation of adversarial samples in every episode of training, which further adds a computational burden. To avoid such time-consuming and complicated procedures, we propose a simple but effective alternative that does not require any adversarial samples. Inspired by the cognitive decision-making process in humans, we enforce high-level feature matching between the base class data and their corresponding low-frequency samples in the pretraining stage via self distillation. The model is then fine-tuned on the samples of novel classes where we additionally improve the discriminability of low-frequency query set features via cosine similarity. On a 1-shot setting of the CIFAR-FS dataset, our method yields a massive improvement of $60.55\%$ & $62.05\%$ in adversarial accuracy on the PGD and state-of-the-art Auto Attack, respectively, with a minor drop in clean accuracy compared to the baseline. Moreover, our method only takes $1.69\times$ of the standard training time while being $\approx$ $5\times$ faster than state-of-the-art adversarial meta-learning methods. The code is available at https://github.com/vcl-iisc/robust-few-shot-learning.

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