Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations

Recent neural architecture search (NAS) frameworks have been successful in finding optimal architectures for given conditions (e.g., performance or latency). However, they search for optimal architectures in terms of their performance on clean images only, while robustness against various types of perturbations or corruptions is crucial in practice. Although there exist several robust NAS frameworks that tackle this issue by integrating adversarial training into one-shot NAS, however, they are limited in that they only consider robustness against adversarial attacks and require significant computational resources to discover optimal architectures for a single task, which makes them impractical in real-world scenarios. To address these challenges, we propose a novel lightweight robust zero-cost proxy that considers the consistency across features, parameters, and gradients of both clean and perturbed images at the initialization state. Our approach facilitates an efficient and rapid search for neural architectures capable of learning generalizable features that exhibit robustness across diverse perturbations. The experimental results demonstrate that our proxy can rapidly and efficiently search for neural architectures that are consistently robust against various perturbations on multiple benchmark datasets and diverse search spaces, largely outperforming existing clean zero-shot NAS and robust NAS with reduced search cost.

Targeted Adversarial Self-Supervised Learning

Recently, unsupervised adversarial training (AT) has been extensively studied to attain robustness with the models trained upon unlabeled data. To this end, previous studies have applied existing supervised adversarial training techniques to self-supervised learning (SSL) frameworks. However, all have resorted to untargeted adversarial learning as obtaining targeted adversarial examples is unclear in the SSL setting lacking of label information. In this paper, we propose a novel targeted adversarial training method for the SSL frameworks. Specifically, we propose a target selection algorithm for the adversarial SSL frameworks; it is designed to select the most confusing sample for each given instance based on similarity and entropy, and perturb the given instance toward the selected target sample. Our method significantly enhances the robustness of an SSL model without requiring large batches of images or additional models, unlike existing works aimed at achieving the same goal. Moreover, our method is readily applicable to general SSL frameworks that only uses positive pairs. We validate our method on benchmark datasets, on which it obtains superior robust accuracies, outperforming existing unsupervised adversarial training methods.

Few-shot Transferable Robust Representation Learning via Bilevel Attacks

Existing adversarial learning methods for enhancing the robustness of deep neural networks assume the availability of a large amount of data from which we can generate adversarial examples. However, in an adversarial meta-learning setting, the model needs to train with only a few adversarial examples to learn a robust model for unseen tasks, which is a very difficult goal to achieve. Further, learning transferable robust representations for unseen domains is a difficult problem even with a large amount of data. To tackle such a challenge, we propose a novel adversarial self-supervised meta-learning framework with bilevel attacks which aims to learn robust representations that can generalize across tasks and domains. Specifically, in the inner loop, we update the parameters of the given encoder by taking inner gradient steps using two different sets of augmented samples, and generate adversarial examples for each view by maximizing the instance classification loss. Then, in the outer loop, we meta-learn the encoder parameter to maximize the agreement between the two adversarial examples, which enables it to learn robust representations. We experimentally validate the effectiveness of our approach on unseen domain adaptation tasks, on which it achieves impressive performance. Specifically, our method significantly outperforms the state-of-the-art meta-adversarial learning methods on few-shot learning tasks, as well as self-supervised learning baselines in standard learning settings with large-scale datasets.