AugLy: Data Augmentations for Robustness
We introduce AugLy, a data augmentation library with a focus on adversarial robustness. AugLy provides a wide array of augmentations for multiple modalities (audio, image, text, & video). These augmentations were inspired by those that real users perform on social media platforms, some of which were not already supported by existing data augmentation libraries. AugLy can be used for any purpose where data augmentations are useful, but it is particularly well-suited for evaluating robustness and systematically generating adversarial attacks. In this paper we present how AugLy works, benchmark it compared against existing libraries, and use it to evaluate the robustness of various state-of-the-art models to showcase AugLy's utility. The AugLy repository can be found at https://github.com/facebookresearch/AugLy.
Code (1)
Tasks
Adversarial RobustnessData AugmentationSimilar Papers 제목 키워드 기반
Enhancing object detection robustness: A synthetic and natural perturbation approach
Robustness against real-world distribution shifts is crucial for the successful deployment of object detection models in practical applications. In this paper, we address the problem of assessing and enhancing the robust…
Data AugmentationObjectobject-detectionObject DetectionIs Robustness Robust? On the interaction between augmentations and corruptions
Invariance to a broad array of image corruptions, such as warping, noise, or color shifts, is an important aspect of building robust models in computer vision. Recently, several new data augmentations have been proposed …
On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness
Invariance to a broad array of image corruptions, such as warping, noise, or color shifts, is an important aspect of building robust models in computer vision. Recently, several new data augmentations have been proposed …
Improving Black-box Robustness with In-Context Rewriting
Machine learning models for text classification often excel on in-distribution (ID) data but struggle with unseen out-of-distribution (OOD) inputs. Most techniques for improving OOD robustness are not applicable to setti…
News Classificationtext-classificationText ClassificationA Flat Minima Perspective on Understanding Augmentations and Model Robustness
Model robustness indicates a model's capability to generalize well on unforeseen distributional shifts, including data corruption, adversarial attacks, and domain shifts. Data augmentation is one of the prevalent and eff…
Adversarial RobustnessData AugmentationDomain Generalization