Adversarially Robust Few-Shot Learning: A Meta-Learning Approach

103 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks (2020, background): “A formal description of this process can be found in Algorithm 1, as seen in (Goldblum et al., 2019a).”
Meta-Learning in Neural Networks: A Survey (2020, background): “the case in domain-shift, one can train the learning algorithm for robustness by defining a meta-loss in terms of performance under adversarial attack [95], [286].”
Advances in adversarial attacks and defenses in computer vision: A survey (2021, methodology): “[442] proposed a method to infer robust models for few-shot classification tasks based on adversarially robust meta-learners.”
Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning (2022, background): “In addition to generalization-ability, recent works (Wang et al., 2021; Goldblum et al., 2020; Xu et al., 2020) investigated MAML from another important perspective of adversarial robustness the capabilities of a model to defend against adversarial perturbed inputs (also known as adversarial…”