537 Repositories
Python unrestricted-adversarial-examples Libraries
Time Series Prediction with tf.contrib.timeseries
TensorFlow-Time-Series-Examples Additional examples for TensorFlow Time Series(TFTS). Read a Time Series with TFTS From a Numpy Array: See "test_input
Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks
SSRL-for-image-classification Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks
Pytorch implementation of forward and inverse Haar Wavelets 2D
Pytorch implementation of forward and inverse Haar Wavelets 2D
[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data
Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data (NeurIPS 2021) This repository will provide the official PyTorch implementa
⚖️🔁🔮🕵️♂️🦹🖼️ Code for *Measuring the Contribution of Multiple Model Representations in Detecting Adversarial Instances* paper.
Measuring the Contribution of Multiple Model Representations in Detecting Adversarial Instances This repository contains the code for Measuring the Co
Generating Band-Limited Adversarial Surfaces Using Neural Networks
Generating Band-Limited Adversarial Surfaces Using Neural Networks This is the official repository of the technical report that was published on arXiv
Improving the robustness and performance of biomedical NLP models through adversarial training
RobustBioNLP Improving the robustness and performance of biomedical NLP models through adversarial training In this repository you can find suppliment
Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region Fitting
Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region Fitting
Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)
Learning to Adapt Structured Output Space for Semantic Segmentation Pytorch implementation of our method for adapting semantic segmentation from the s
Chainer Implementation of Semantic Segmentation using Adversarial Networks
Semantic Segmentation using Adversarial Networks Requirements Chainer (1.23.0) Differences Use of FCN-VGG16 instead of Dilated8 as Segmentor. Caution
Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
Python library with Neural Networks for Image Segmentation based on Keras and TensorFlow. The main features of this library are: High level API (just
A graph adversarial learning toolbox based on PyTorch and DGL.
GraphWar: Arms Race in Graph Adversarial Learning NOTE: GraphWar is still in the early stages and the API will likely continue to change. 🚀 Installat
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness Code for Paper "Imbalanced Gradients: A Subtle Cause of Overestimated Adv
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack Case study of the FCA. The code can be find in FCA. Cas
The official implementation of A Unified Game-Theoretic Interpretation of Adversarial Robustness.
This repository is the official implementation of A Unified Game-Theoretic Interpretation of Adversarial Robustness. Requirements pip install -r requi
Graph Robustness Benchmark: A scalable, unified, modular, and reproducible benchmark for evaluating the adversarial robustness of Graph Machine Learning.
Homepage | Paper | Datasets | Leaderboard | Documentation Graph Robustness Benchmark (GRB) provides scalable, unified, modular, and reproducible evalu
Code for our EMNLP 2021 paper "Learning Kernel-Smoothed Machine Translation with Retrieved Examples"
KSTER Code for our EMNLP 2021 paper "Learning Kernel-Smoothed Machine Translation with Retrieved Examples" [paper]. Usage Download the processed datas
Code for paper "Role-oriented Network Embedding Based on Adversarial Learning between Higher-order and Local Features"
Role-oriented Network Embedding Based on Adversarial Learning between Higher-order and Local Features Train python main.py --dataset brazil-flights C
Algorand Python API examples
Algorand-Py Algorand Python API examples This repo will hold example scripts to monitor activities on Algorand main net. You can: Monitor your assets
Code for "Adversarial attack by dropping information." (ICCV 2021)
AdvDrop Code for "AdvDrop: Adversarial Attack to DNNs by Dropping Information(ICCV 2021)." Human can easily recognize visual objects with lost informa
code for "Feature Importance-aware Transferable Adversarial Attacks"
Feature Importance-aware Attack(FIA) This repository contains the code for the paper: Feature Importance-aware Transferable Adversarial Attacks (ICCV
Code repository for EMNLP 2021 paper 'Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods'
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods This is the code repository to accompany the EMNLP 2021 paper on ad
Autonomous Ground Vehicle Navigation and Control Simulation Examples in Python
Autonomous Ground Vehicle Navigation and Control Simulation Examples in Python THIS PROJECT IS CURRENTLY A WORK IN PROGRESS AND THUS THIS REPOSITORY I
Adversarial Graph Augmentation to Improve Graph Contrastive Learning
ADGCL : Adversarial Graph Augmentation to Improve Graph Contrastive Learning Introduction This repo contains the Pytorch [1] implementation of Adversa
This library is testing the ethics of language models by using natural adversarial texts.
prompt2slip This library is testing the ethics of language models by using natural adversarial texts. This tool allows for short and simple code and v
Official repository for the NeurIPS 2021 paper Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided curriculum Learning Approach
Get Fooled for the Right Reason Official repository for the NeurIPS 2021 paper Get Fooled for the Right Reason: Improving Adversarial Robustness throu
This repository contains the code used to quantitatively evaluate counterfactual examples in the associated paper.
On Quantitative Evaluations of Counterfactuals Install To install required packages with conda, run the following command: conda env create -f requi
The code for the NeurIPS 2021 paper "A Unified View of cGANs with and without Classifiers".
Energy-based Conditional Generative Adversarial Network (ECGAN) This is the code for the NeurIPS 2021 paper "A Unified View of cGANs with and without
Companion code for the paper "Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks" by Yatsura et al.
META-RS This is the companion code for the paper "Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks" by Yatsu
Generative Adversarial Networks(GANs)
Generative Adversarial Networks(GANs) Vanilla GAN ClusterGAN Vanilla GAN Model Structure Final Generator Structure A MLP with 2 hidden layers of hidde
Code for "Adversarial Attack Generation Empowered by Min-Max Optimization", NeurIPS 2021
Min-Max Adversarial Attacks [Paper] [arXiv] [Video] [Slide] Adversarial Attack Generation Empowered by Min-Max Optimization Jingkang Wang, Tianyun Zha
Data Poisoning based on Adversarial Attacks using Non-Robust Features
Data Poisoning based on Adversarial Attacks using Non-Robust Features Usage python main.py [-h] [--gpu | -g GPU] [--eps |-e EPSILON] [--pert | -p PER
Enhancing Knowledge Tracing via Adversarial Training
Enhancing Knowledge Tracing via Adversarial Training This repository contains source code for the paper "Enhancing Knowledge Tracing via Adversarial T
Pytorch Implementation of Adversarial Deep Network Embedding for Cross-Network Node Classification
Pytorch Implementation of Adversarial Deep Network Embedding for Cross-Network Node Classification (ACDNE) This is a pytorch implementation of the Adv
Implementation of average- and worst-case robust flatness measures for adversarial training.
Relating Adversarially Robust Generalization to Flat Minima This repository contains code corresponding to the MLSys'21 paper: D. Stutz, M. Hein, B. S
🔥 Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization 🔥
🔥 Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization 🔥
Short PhD seminar on Machine Learning Security (Adversarial Machine Learning)
Short PhD seminar on Machine Learning Security (Adversarial Machine Learning)
Chinese NER(Named Entity Recognition) using BERT(Softmax, CRF, Span)
Chinese NER(Named Entity Recognition) using BERT(Softmax, CRF, Span)
SimplEx - Explaining Latent Representations with a Corpus of Examples
SimplEx - Explaining Latent Representations with a Corpus of Examples Code Author: Jonathan Crabbé ([email protected]) This repository contains the imp
Adversarial Robustness with Non-uniform Perturbations
Adversarial Robustness with Non-uniform Perturbations This repository hosts the code to replicate experiments of the paper Adversarial Robustness with
AugMax: Adversarial Composition of Random Augmentations for Robust Training
[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.
Unofficial implement with paper SpeakerGAN: Speaker identification with conditional generative adversarial network
Introduction This repository is about paper SpeakerGAN , and is unofficially implemented by Mingming Huang ([email protected]), Tiezheng Wang (wtz920729
[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.
AugMax: Adversarial Composition of Random Augmentations for Robust Training Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, an
[NeurIPS 2021] Source code for the paper "Qu-ANTI-zation: Exploiting Neural Network Quantization for Achieving Adversarial Outcomes"
Qu-ANTI-zation This repository contains the code for reproducing the results of our paper: Qu-ANTI-zation: Exploiting Quantization Artifacts for Achie
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
Some toy examples of score matching algorithms written in PyTorch
toy_gradlogp This repo implements some toy examples of the following score matching algorithms in PyTorch: ssm-vr: sliced score matching with variance
[NeurIPS 2021] Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training
Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training Code for NeurIPS 2021 paper "Better Safe Than Sorry: Preventing Delu
Spectacular AI SDK fuses data from cameras and IMU sensors and outputs an accurate 6-degree-of-freedom pose of a device.
Spectacular AI SDK examples Spectacular AI SDK fuses data from cameras and IMU sensors (accelerometer and gyroscope) and outputs an accurate 6-degree-
Estimating Example Difficulty using Variance of Gradients
Estimating Example Difficulty using Variance of Gradients This repository contains source code necessary to reproduce some of the main results in the
This code uses generative adversarial networks to generate diverse task allocation plans for Multi-agent teams.
Mutli-agent task allocation This code uses generative adversarial networks to generate diverse task allocation plans for Multi-agent teams. To change
Code for "Generative adversarial networks for reconstructing natural images from brain activity".
Reconstruct handwritten characters from brains using GANs Example code for the paper "Generative adversarial networks for reconstructing natural image
Unit testing AWS interactions with pytest and moto. These examples demonstrate how to structure, setup, teardown, mock, and conduct unit testing. The source code is only intended to demonstrate unit testing.
Unit Testing Interactions with Amazon Web Services (AWS) Unit testing AWS interactions with pytest and moto. These examples demonstrate how to structu
A sentence search engine that fetches examples from trusted news/media organisations. Great for writing better English.
A sentence search engine that fetches examples from trusted news/media websites. Great for improving writing & speaking better English.
This repository contains all source code, pre-trained models related to the paper "An Empirical Study on GANs with Margin Cosine Loss and Relativistic Discriminator"
An Empirical Study on GANs with Margin Cosine Loss and Relativistic Discriminator This is a Pytorch implementation for the paper "An Empirical Study o
NeurIPS'21: Probabilistic Margins for Instance Reweighting in Adversarial Training (Pytorch implementation).
source code for NeurIPS21 paper robabilistic Margins for Instance Reweighting in Adversarial Training
Flickr-Faces-HQ (FFHQ) is a high-quality image dataset of human faces, originally created as a benchmark for generative adversarial networks (GAN)
Flickr-Faces-HQ Dataset (FFHQ) Flickr-Faces-HQ (FFHQ) is a high-quality image dataset of human faces, originally created as a benchmark for generative
Implements the training, testing and editing tools for "Pluralistic Image Completion"
Pluralistic Image Completion ArXiv | Project Page | Online Demo | Video(demo) This repository implements the training, testing and editing tools for "
The Self-Supervised Learner can be used to train a classifier with fewer labeled examples needed using self-supervised learning.
Published by SpaceML • About SpaceML • Quick Colab Example Self-Supervised Learner The Self-Supervised Learner can be used to train a classifier with
Codebase for Time-series Generative Adversarial Networks (TimeGAN)
Codebase for Time-series Generative Adversarial Networks (TimeGAN)
Pytorch implementation of the paper Time-series Generative Adversarial Networks
TimeGAN-pytorch Pytorch implementation of the paper Time-series Generative Adversarial Networks presented at NeurIPS'19. Jinsung Yoon, Daniel Jarrett
Forecasting Nonverbal Social Signals during Dyadic Interactions with Generative Adversarial Neural Networks
ForecastingNonverbalSignals This is the implementation for the paper Forecasting Nonverbal Social Signals during Dyadic Interactions with Generative A
3D-aware GANs based on NeRF (arXiv).
CIPS-3D This repository will contain the code of the paper, CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis.
🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation
This repository contains the code accompanying the paper " FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation" Paper link: R
[ICCV 2021] Focal Frequency Loss for Image Reconstruction and Synthesis
Focal Frequency Loss - Official PyTorch Implementation This repository provides the official PyTorch implementation for the following paper: Focal Fre
CR-Fill: Generative Image Inpainting with Auxiliary Contextual Reconstruction. ICCV 2021
crfill Usage | Web App | | Paper | Supplementary Material | More results | code for paper ``CR-Fill: Generative Image Inpainting with Auxiliary Contex
A Python library for adversarial machine learning focusing on benchmarking adversarial robustness.
ARES This repository contains the code for ARES (Adversarial Robustness Evaluation for Safety), a Python library for adversarial machine learning rese
Code for SALT: Stackelberg Adversarial Regularization, EMNLP 2021.
SALT: Stackelberg Adversarial Regularization Code for Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach, EMNLP 2021. R
Python code to generate art with Generative Adversarial Network
GAN_Canvas_Maker Generating Art using Generative Adversarial Network (GAN) Python code to generate art with Generative Adversarial Network: https://to
PyTorch implementation of our method for adversarial attacks and defenses in hyperspectral image classification.
Self-Attention Context Network for Hyperspectral Image Classification PyTorch implementation of our method for adversarial attacks and defenses in hyp
Boosting Adversarial Attacks with Enhanced Momentum (BMVC 2021)
EMI-FGSM This repository contains code to reproduce results from the paper: Boosting Adversarial Attacks with Enhanced Momentum (BMVC 2021) Xiaosen Wa
A tutorial presents several practical examples of how to build DAGs in Apache Airflow
Apache Airflow - Python Brasil 2021 Este tutorial apresenta vários exemplos práticos de como construir DAGs no Apache Airflow. Background Apache Airfl
Unsupervised Image Generation with Infinite Generative Adversarial Networks
Unsupervised Image Generation with Infinite Generative Adversarial Networks Here is the implementation of MICGANs using DCGAN architecture on MNIST da
CrayLabs and user contibuted examples of using SmartSim for various simulation and machine learning applications.
SmartSim Example Zoo This repository contains CrayLabs and user contibuted examples of using SmartSim for various simulation and machine learning appl
A style-based Quantum Generative Adversarial Network
Style-qGAN A style based Quantum Generative Adversarial Network (style-qGAN) model for Monte Carlo event generation. Tutorial We have prepared a noteb
Code and data of the EMNLP 2021 paper "Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer"
StyleAttack Code and data of the EMNLP 2021 paper "Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer" Prepare Pois
Pytorch implementation of the paper: "A Unified Framework for Separating Superimposed Images", in CVPR 2020.
Deep Adversarial Decomposition PDF | Supp | 1min-DemoVideo Pytorch implementation of the paper: "Deep Adversarial Decomposition: A Unified Framework f
source code of Adversarial Feedback Loop Paper
Adversarial Feedback Loop [ArXiv] [project page] Official repository of Adversarial Feedback Loop paper Firas Shama, Roey Mechrez, Alon Shoshan, Lihi
Code for the preprint "Well-classified Examples are Underestimated in Classification with Deep Neural Networks"
This is a repository for the paper of "Well-classified Examples are Underestimated in Classification with Deep Neural Networks" The implementation and
Code repository for the paper "Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation" with instructions to reproduce the results.
Doubly Trained Neural Machine Translation System for Adversarial Attack and Data Augmentation Languages Experimented: Data Overview: Source Target Tra
Source code for the paper "SEPP: Similarity Estimation of Predicted Probabilities for Defending and Detecting Adversarial Text" PACLIC 2021
Adversarial text generator Refer to "adversarial_text_generator"[https://github.com/quocnsh/SEPP_generator] project for generating adversarial texts A
Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation
Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation Paper Multi-Target Adversarial Frameworks for Domain Adaptation in
A collection of testing examples using pytest and many other libreris
Effective testing with Python This project was created for PyConEs 2021 Check out the test samples at tests Check out the slides at slides (markdown o
FAST-RIR: FAST NEURAL DIFFUSE ROOM IMPULSE RESPONSE GENERATOR
This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.
This book will take you on an exploratory journey through the PDF format, and the borb Python library.
This book will take you on an exploratory journey through the PDF format, and the borb Python library.
Alias-Free Generative Adversarial Networks (StyleGAN3) Official PyTorch implementation
Alias-Free Generative Adversarial Networks (StyleGAN3) Official PyTorch implementation
NVIDIA Deep Learning Examples for Tensor Cores
NVIDIA Deep Learning Examples for Tensor Cores Introduction This repository provides State-of-the-Art Deep Learning examples that are easy to train an
Examples of usage of GStreamer hlssink3 plugin.
Examples of usage of GStreamer hlssink3 plugin.
Beyonic API Python official client library simplified examples using Flask, Django and Fast API.
Beyonic API Python official client library simplified examples using Flask, Django and Fast API.
A set of examples around hub for creating and processing datasets
Examples for Hub - Dataset Format for AI A repository showcasing examples of using Hub Uploading Dataset Places365 Colab Tutorials Notebook Link Getti
Beyonic API Python official client library simplified examples using Flask, Django and Fast API.
Beyonic API Python Examples. The beyonic APIs Doc Reference: https://apidocs.beyonic.com/ To start using the Beyonic API Python API, you need to start
This repository contains the code for the CVPR 2021 paper "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields"
GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields Project Page | Paper | Supplementary | Video | Slides | Blog | Talk If
Bald-to-Hairy Translation Using CycleGAN
GANiry: Bald-to-Hairy Translation Using CycleGAN Official PyTorch implementation of GANiry. GANiry: Bald-to-Hairy Translation Using CycleGAN, Fidan Sa
Transfer-Learn is an open-source and well-documented library for Transfer Learning.
Transfer-Learn is an open-source and well-documented library for Transfer Learning. It is based on pure PyTorch with high performance and friendly API. Our code is pythonic, and the design is consistent with torchvision. You can easily develop new algorithms, or readily apply existing algorithms.
Pytorch implementation of four neural network based domain adaptation techniques: DeepCORAL, DDC, CDAN and CDAN+E. Evaluated on benchmark dataset Office31.
Deep-Unsupervised-Domain-Adaptation Pytorch implementation of four neural network based domain adaptation techniques: DeepCORAL, DDC, CDAN and CDAN+E.
GANimation: Anatomically-aware Facial Animation from a Single Image (ECCV'18 Oral) [PyTorch]
GANimation: Anatomically-aware Facial Animation from a Single Image [Project] [Paper] Official implementation of GANimation. In this work we introduce
PyTorch code accompanying our paper on Maximum Entropy Generators for Energy-Based Models
Maximum Entropy Generators for Energy-Based Models All experiments have tensorboard visualizations for samples / density / train curves etc. To run th
(ICCV 2021) Official code of "Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing."
Dressing in Order (DiOr) 👚 [Paper] 👖 [Webpage] 👗 [Running this code] The official implementation of "Dressing in Order: Recurrent Person Image Gene
This is the official code for the paper "Ad2Attack: Adaptive Adversarial Attack for Real-Time UAV Tracking".
Ad^2Attack:Adaptive Adversarial Attack on Real-Time UAV Tracking Demo video 📹 Our video on bilibili demonstrates the test results of Ad^2Attack on se
simple generative adversarial network (GAN) using PyTorch
Generative Adversarial Networks (GANs) in PyTorch Running Run the sample code by typing: ./gan_pytorch.py ...and you'll train two nets to battle it o