74 Open source projects that are alternatives of or similar to Adversarial-Distributional-Training

Code for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.

Feature Scattering Adversarial Training

A Closer Look at Accuracy vs. Robustness

Minimal implementation of Denoised Smoothing (https://arxiv.org/abs/2003.01908) in TensorFlow.

Codes for NeurIPS 2020 paper "Adversarial Weight Perturbation Helps Robust Generalization"

Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation （ICCV2021）

Adversarial Ranking Attack and Defense, ECCV, 2020.

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

Code implementation of the paper "With Great Training Comes Great Vulnerability: Practical Attacks against Transfer Learning", at USENIX Security 2018

PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL

Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs

Task-agnostic universal black-box attacks on computer vision neural network via procedural noise (CCS'19)

Personalized Federated Learning with Moreau Envelopes (pFedMe) using Pytorch (NeurIPS 2020)

reproduction of Thermometer Encoding: One Hot Way To Resist Adversarial Examples in pytorch

Code and data for the ICLR 2021 paper "Perceptual Adversarial Robustness: Defense Against Unseen Threat Models".

PyTorch code of "Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows" (NeurIPS 2020)

Source code for the ICLR 2021 work "Generating Adversarial Computer Programs using Optimized Obfuscations"

Implementation of the paper "Certifiable Robustness and Robust Training for Graph Convolutional Networks".

Sensor data aggregation tool for any numerical sensor data. Robust and privacy-friendly.

Figures & code from the paper "Shortcut Learning in Deep Neural Networks" (Nature Machine Intelligence 2020)

关于domain generalization，domain adaptation，causality，robutness，prompt，optimization，generative model各式各样研究的阅读笔记

Robustness benchmark for DNN models.

Repo for "3D-PV-Locator: Large-scale detection of rooftop-mounted photovoltaic systems in 3D" based on Applied Energy publication.

Code used during my Chaos Engineering and Resiliency Patterns talk.

An Algorithm to Quantify Robustness of Recurrent Neural Networks

Implementation of the WSDM 2021 paper "Node Similarity Preserving Graph Convolutional Networks"

Contains code for the paper "Vision Transformers are Robust Learners" (AAAI 2022).

Python toolbox to evaluate graph vulnerability and robustness (CIKM 2021)

[NeurIPS 2021]: Are Transformers More Robust Than CNNs? (Pytorch implementation & checkpoints)

Scaleable input gradient regularization

KitanaQA: Adversarial training and data augmentation for neural question-answering models

Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)

Reachability analysis for closed-loop control systems

Robust error-handling for Kotlin and Android

Benchmarks for Corruption Invariant Person Re-identification. [NeurIPS 2021 Track on Datasets and Benchmarks]

Resources for the paper titled "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network". Accepted for publication (with an oral spotlight!) at ML4H Workshop, NeurIPS 2020.

Athena: A Framework for Defending Machine Learning Systems Against Adversarial Attacks

[CVPR 2022] S-attack library. Official implementation of two papers "Vehicle trajectory prediction works, but not everywhere" and "Are socially-aware trajectory prediction models really socially-aware?".

SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression

The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-…

Consistency Regularization for Adversarial Robustness (AAAI 2022)

[NeurIPS'2019] Shupeng Gui, Haotao Wang, Haichuan Yang, Chen Yu, Zhangyang Wang, Ji Liu, “Model Compression with Adversarial Robustness: A Unified Optimization Framework”

Winning Solution of the NeurIPS 2020 Competition on Predicting Generalization in Deep Learning

Code for AAAI 2018 accepted paper: "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients"

Code for paper "Model-based Adversarial Meta-Reinforcement Learning" (https://arxiv.org/abs/2006.08875)

阿里云MNS

Language-Adversarial Training for Cross-Lingual Text Classification (TACL)

Code for NeurIPS 2019 Paper

A curated (most recent) list of resources for Learning with Noisy Labels

RayS: A Ray Searching Method for Hard-label Adversarial Attack (KDD2020)

(ICML 2022) Official PyTorch implementation of “Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness”.

Meta-Learning through Hebbian Plasticity in Random Networks: https://arxiv.org/abs/2007.02686

My entry for ICLR 2018 Reproducibility Challenge for paper Synthesizing robust adversarial examples https://openreview.net/pdf?id=BJDH5M-AW

Code and models for ICCV2021 paper "Robust Object Detection via Instance-Level Temporal Cycle Confusion".

Recentrifuge: robust comparative analysis and contamination removal for metagenomics

Attacking Vision based Perception in End-to-end Autonomous Driving Models

Backdoors Framework for Deep Learning and Federated Learning. A light-weight tool to conduct your research on backdoors.

Source code and dataset for the CCKS201 paper "On Robustness and Bias Analysis of BERT-based Relation Extraction"

A curated list of awesome resources regarding machine learning reliability.

Code for the paper "Strongly Incremental Constituency Parsing with Graph Neural Networks"