478 Repositories
Python conditional-adversarial-da Libraries
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).
[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
PyTorch Implementation of ByteDance's Cross-speaker Emotion Transfer Based on Speaker Condition Layer Normalization and Semi-Supervised Training in Text-To-Speech
Cross-Speaker-Emotion-Transfer - PyTorch Implementation PyTorch Implementation of ByteDance's Cross-speaker Emotion Transfer Based on Speaker Conditio
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
Official code for MPG2: Multi-attribute Pizza Generator: Cross-domain Attribute Control with Conditional StyleGAN
This is the official code for Multi-attribute Pizza Generator (MPG2): Cross-domain Attribute Control with Conditional StyleGAN. Paper Demo Setup Envir
Code implementation for the paper 'Conditional Gaussian PAC-Bayes'.
CondGauss This repository contains PyTorch code for the paper Stochastic Gaussian PAC-Bayes. A novel PAC-Bayesian training method is implemented. Ther
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
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
Official implementation of the paper Chunked Autoregressive GAN for Conditional Waveform Synthesis
Chunked Autoregressive GAN (CARGAN) Official implementation of the paper Chunked Autoregressive GAN for Conditional Waveform Synthesis [paper] [compan
PyTorch implementation of D2C: Diffuison-Decoding Models for Few-shot Conditional Generation.
D2C: Diffuison-Decoding Models for Few-shot Conditional Generation Project | Paper PyTorch implementation of D2C: Diffuison-Decoding Models for Few-sh
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 "
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
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
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
Datasets accompanying the paper ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers.
ConditionalQA Datasets accompanying the paper ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers. Disclaimer This dataset
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
Continuous Conditional Random Field Convolution for Point Cloud Segmentation
CRFConv This repository is the implementation of "Continuous Conditional Random Field Convolution for Point Cloud Segmentation" 1. Setup 1) Building c
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
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.
[ICCV'2021] Image Inpainting via Conditional Texture and Structure Dual Generation
[ICCV'2021] Image Inpainting via Conditional Texture and Structure Dual Generation
Alias-Free Generative Adversarial Networks (StyleGAN3) Official PyTorch implementation
Alias-Free Generative Adversarial Networks (StyleGAN3) Official PyTorch implementation
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
C++ Implementation of PyTorch Tutorials for Everyone
C++ Implementation of PyTorch Tutorials for Everyone OS (Compiler)\LibTorch 1.9.0 macOS (clang 10.0, 11.0, 12.0) Linux (gcc 8, 9, 10, 11) Windows (msv
CondNet: Conditional Classifier for Scene Segmentation
CondNet: Conditional Classifier for Scene Segmentation Introduction The fully convolutional network (FCN) has achieved tremendous success in dense vis
Code for ACL'2021 paper WARP 🌀 Word-level Adversarial ReProgramming
Code for ACL'2021 paper WARP 🌀 Word-level Adversarial ReProgramming. Outperforming `GPT-3` on SuperGLUE Few-Shot text classification.
Conditional probing: measuring usable information beyond a baseline
Conditional probing: measuring usable information beyond a baseline
Universal Adversarial Triggers for Attacking and Analyzing NLP (EMNLP 2019)
Universal Adversarial Triggers for Attacking and Analyzing NLP This is the official code for the EMNLP 2019 paper, Universal Adversarial Triggers for
Adversarial Framework for (non-) Parametric Image Stylisation Mosaics
Fully Adversarial Mosaics (FAMOS) Pytorch implementation of the paper "Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Imag
Image-to-Image Translation in PyTorch
CycleGAN and pix2pix in PyTorch New: Please check out contrastive-unpaired-translation (CUT), our new unpaired image-to-image translation model that e
Code for the paper "Adversarial Generator-Encoder Networks"
This repository contains code for the paper "Adversarial Generator-Encoder Networks" (AAAI'18) by Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky. Pr
A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing.
AnimeGAN A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing. Randomly Generated Images The images are
Official implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"
DiscoGAN Official PyTorch implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. Prerequisites Python 2.7
PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"
DiscoGAN in PyTorch PyTorch implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. * All samples in READM
PyTorch implementation of "Image-to-Image Translation Using Conditional Adversarial Networks".
pix2pix-pytorch PyTorch implementation of Image-to-Image Translation Using Conditional Adversarial Networks. Based on pix2pix by Phillip Isola et al.
TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP
TextAttack 🐙 Generating adversarial examples for NLP models [TextAttack Documentation on ReadTheDocs] About • Setup • Usage • Design About TextAttack
A2T: Towards Improving Adversarial Training of NLP Models (EMNLP 2021 Findings)
A2T: Towards Improving Adversarial Training of NLP Models This is the source code for the EMNLP 2021 (Findings) paper "Towards Improving Adversarial T
G-NIA model from "Single Node Injection Attack against Graph Neural Networks" (CIKM 2021)
Single Node Injection Attack against Graph Neural Networks This repository is our Pytorch implementation of our paper: Single Node Injection Attack ag
RDA: Robust Domain Adaptation via Fourier Adversarial Attacking
RDA: Robust Domain Adaptation via Fourier Adversarial Attacking Updates 08/2021: check out our domain adaptation for video segmentation paper Domain A
Code for Blind Image Decomposition (BID) and Blind Image Decomposition network (BIDeN).
arXiv, porject page, paper Blind Image Decomposition (BID) Blind Image Decomposition is a novel task. The task requires separating a superimposed imag
PrimitiveNet: Primitive Instance Segmentation with Local Primitive Embedding under Adversarial Metric (ICCV 2021)
PrimitiveNet Source code for the paper: Jingwei Huang, Yanfeng Zhang, Mingwei Sun. [PrimitiveNet: Primitive Instance Segmentation with Local Primitive
Official PyTorch code for Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (HCFlow, ICCV2021)
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (HCFlow, ICCV2021) This repository is the official P
Official implementation of the MM'21 paper Constrained Graphic Layout Generation via Latent Optimization
[MM'21] Constrained Graphic Layout Generation via Latent Optimization This repository provides the official code for the paper "Constrained Graphic La
The source code of CVPR 2019 paper "Deep Exemplar-based Video Colorization".
Deep Exemplar-based Video Colorization (Pytorch Implementation) Paper | Pretrained Model | Youtube video 🔥 | Colab demo Deep Exemplar-based Video Col
Official PyTorch code for Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (HCFlow, ICCV2021)
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (HCFlow, ICCV2021) This repository is the official P
Code for "On the Effects of Batch and Weight Normalization in Generative Adversarial Networks"
Note: this repo has been discontinued, please check code for newer version of the paper here Weight Normalized GAN Code for the paper "On the Effects
pytorch implementation for Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network arXiv:1609.04802
PyTorch SRResNet Implementation of Paper: "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"(https://arxiv.org/abs
A PyTorch implementation of the paper "Semantic Image Synthesis via Adversarial Learning" in ICCV 2017
Semantic Image Synthesis via Adversarial Learning This is a PyTorch implementation of the paper Semantic Image Synthesis via Adversarial Learning. Req
Collection of generative models in Pytorch version.
pytorch-generative-model-collections Original : [Tensorflow version] Pytorch implementation of various GANs. This repository was re-implemented with r
Official PyTorch code for WACV 2022 paper "CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows"
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows WACV 2022 preprint:https://arxiv.org/abs/2107.1
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-a2c-ppo-acktr Update (April 12th, 2021) PPO is great, but Soft Actor Critic can be better for many continuous control tasks. Please check out
Official implementation of the ICCV 2021 paper "Conditional DETR for Fast Training Convergence".
The DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings and that the spatial embeddings make minor contributions, increasing the need for high-quality content embeddings and thus increasing the training difficulty.
Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation (ICCV2021)
Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation This is a pytorch project for the paper Dynamic Divide-and-Conquer Ad
Pytorch implementation for reproducing StackGAN_v2 results in the paper StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
StackGAN-v2 StackGAN-v1: Tensorflow implementation StackGAN-v1: Pytorch implementation Inception score evaluation Pytorch implementation for reproduci
Defending graph neural networks against adversarial attacks (NeurIPS 2020)
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks Authors: Xiang Zhang ([email protected]), Marinka Zitnik (marinka@hms.
Partial implementation of ODE-GAN technique from the paper Training Generative Adversarial Networks by Solving Ordinary Differential Equations
ODE GAN (Prototype) in PyTorch Partial implementation of ODE-GAN technique from the paper Training Generative Adversarial Networks by Solving Ordinary
Zero-Shot Text-to-Image Generation VQGAN+CLIP Dockerized
VQGAN-CLIP-Docker About Zero-Shot Text-to-Image Generation VQGAN+CLIP Dockerized This is a stripped and minimal dependency repository for running loca
Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks This is a Python3 / Pytorch implementation of TadGAN paper. The associated
Implementation detail for paper "Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet"
Multi-level-colonoscopy-malignant-tissue-detection-with-adversarial-CAC-UNet Implementation detail for our paper "Multi-level colonoscopy malignant ti
Image Deblurring using Generative Adversarial Networks
DeblurGAN arXiv Paper Version Pytorch implementation of the paper DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks. Our netwo
Synthesizing and manipulating 2048x1024 images with conditional GANs
pix2pixHD Project | Youtube | Paper Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic image-to-image translatio
PyTorch implementation of adversarial patch
adversarial-patch PyTorch implementation of adversarial patch This is an implementation of the Adversarial Patch paper. Not official and likely to hav
PyTorch implementations of Generative Adversarial Networks.
This repository has gone stale as I unfortunately do not have the time to maintain it anymore. If you would like to continue the development of it as
T2F: text to face generation using Deep Learning
⭐ [NEW] ⭐ T2F - 2.0 Teaser (coming soon ...) Please note that all the faces in the above samples are generated ones. The T2F 2.0 will be using MSG-GAN
Compute FID scores with PyTorch.
FID score for PyTorch This is a port of the official implementation of Fréchet Inception Distance to PyTorch. See https://github.com/bioinf-jku/TTUR f
Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
Overview PyTorch 0.4.1 | Python 3.6.5 Annotated implementations with comparative introductions for minimax, non-saturating, wasserstein, wasserstein g
Adversarial Framework for (non-) Parametric Image Stylisation Mosaics
Fully Adversarial Mosaics (FAMOS) Pytorch implementation of the paper "Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Imag
A certifiable defense against adversarial examples by training neural networks to be provably robust
DiffAI v3 DiffAI is a system for training neural networks to be provably robust and for proving that they are robust. The system was developed for the
A collection of 100 Deep Learning images and visualizations
A collection of Deep Learning images and visualizations. The project has been developed by the AI Summer team and currently contains almost 100 images.
A collection of 100 Deep Learning images and visualizations
A collection of Deep Learning images and visualizations. The project has been developed by the AI Summer team and currently contains almost 100 images.