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robustness
A library for experimenting with, training and evaluating neural networks, with a focus on adversarial robustness.mnist_challenge
A challenge to explore adversarial robustness of neural networks on MNIST.cifar10_challenge
A challenge to explore adversarial robustness of neural networks on CIFAR10.photoguard
Raising the Cost of Malicious AI-Powered Image Editingconstructed-datasets
Datasets for the paper "Adversarial Examples are not Bugs, They Are Features"trak
A fast, effective data attribution method for neural networks in PyTorchrobust_representations
Code for "Learning Perceptually-Aligned Representations via Adversarial Robustness"backgrounds_challenge
robustness_applications
Notebooks for reproducing the paper "Computer Vision with a Single (Robust) Classifier"implementation-matters
EditingClassifiers
robust-features-code
Code for "Robustness May Be at Odds with Accuracy"datamodels-data
Data for "Datamodels: Predicting Predictions with Training Data"blackbox-bandits
Code for "Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors"BREEDS-Benchmarks
cox
A lightweight experimental logging libraryadversarial_spatial
Investigating the robustness of state-of-the-art CNN architectures to simple spatial transformations.modeldiff
ModelDiff: A Framework for Comparing Learning Algorithmsfailure-directions
Distilling Model Failures as Directions in Latent Spacesmoothed-vit
Certified Patch Robustness via Smoothed Vision Transformerslabel-consistent-backdoor-code
Code for "Label-Consistent Backdoor Attacks"dataset-interfaces
Dataset Interfaces: Diagnosing Model Failures Using Controllable Counterfactual GenerationDebuggableDeepNetworks
data-transfer
ImageNetMultiLabel
Fine-grained ImageNet annotationsrelu_stable
spatial-pytorch
Codebase for "Exploring the Landscape of Spatial Robustness" (ICML'19, https://arxiv.org/abs/1712.02779).dataset-replication-analysis
backdoor_data_poisoning
glm_saga
Minimal, standalone library for solving GLMs in PyTorchAdvEx_Tutorial
rethinking-backdoor-attacks
robustness_lib
journey-TRAK
Code for the paper "The Journey, Not the Destination: How Data Guides Diffusion Models"datamodels
rla
Residue Level Alignmentcopriors
Combining Diverse Feature Priorsmissingness
Code for our ICLR 2022 paper "Missingness Bias in Model Debugging"fast_l1
pytorch-lightning-imagenet
post--adv-discussion
AIaaS_Supply_Chains
Dataset and overviewpytorch-example-imagenet
mnist_challenge_models
robust_model_colab
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