تقرير
Robust Few-shot Learning Without Using any Adversarial Samples
العنوان: | Robust Few-shot Learning Without Using any Adversarial Samples |
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المؤلفون: | Nayak, Gaurav Kumar, Rawal, Ruchit, Khatri, Inder, Chakraborty, Anirban |
سنة النشر: | 2022 |
المجموعة: | Computer Science |
مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning |
الوصف: | 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. Comment: TNNLS Submission (Under Review) |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2211.01598 |
رقم الأكسشن: | edsarx.2211.01598 |
قاعدة البيانات: | arXiv |
الوصف غير متاح. |