1014 Repositories
Python adversarial-training Libraries
Exponential Graph is Provably Efficient for Decentralized Deep Training
Exponential Graph is Provably Efficient for Decentralized Deep Training This code repository is for the paper Exponential Graph is Provably Efficient
[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
In this project I played with mlflow, streamlit and fastapi to create a training and prediction app on digits
Fastapi + MLflow + streamlit Setup env. I hope I covered all. pip install -r requirements.txt Start app Go in the root dir and run these Streamlit str
Asterisk is a framework to generate high-quality training datasets at scale
Asterisk is a framework to generate high-quality training datasets at scale
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
code for generating data set ES-ImageNet with corresponding training code
es-imagenet-master code for generating data set ES-ImageNet with corresponding training code dataset generator some codes of ODG algorithm The variabl
A Context-aware Visual Attention-based training pipeline for Object Detection from a Webpage screenshot!
CoVA: Context-aware Visual Attention for Webpage Information Extraction Abstract Webpage information extraction (WIE) is an important step to create k
Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".
CoProtector Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".
Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".
CoProtector Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".
This is the official source code for SLATE. We provide the code for the model, the training code, and a dataset loader for the 3D Shapes dataset. This code is implemented in Pytorch.
SLATE This is the official source code for SLATE. We provide the code for the model, the training code and a dataset loader for the 3D Shapes dataset.
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
PyTorch Code for NeurIPS 2021 paper Anti-Backdoor Learning: Training Clean Models on Poisoned Data.
Anti-Backdoor Learning PyTorch Code for NeurIPS 2021 paper Anti-Backdoor Learning: Training Clean Models on Poisoned Data. Check the unlearning effect
Efficient Training of Visual Transformers with Small Datasets
Official codes for "Efficient Training of Visual Transformers with Small Datasets", NerIPS 2021.
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
Training Very Deep Neural Networks Without Skip-Connections
DiracNets v2 update (January 2018): The code was updated for DiracNets-v2 in which we removed NCReLU by adding per-channel a and b multipliers without
Training RNNs as Fast as CNNs
News SRU++, a new SRU variant, is released. [tech report] [blog] The experimental code and SRU++ implementation are available on the dev branch which
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
CVNets: A library for training computer vision networks
CVNets: A library for training computer vision networks This repository contains the source code for training computer vision models. Specifically, it
BERT model training impelmentation using 1024 A100 GPUs for MLPerf Training v1.1
Pre-trained checkpoint and bert config json file Location of checkpoint and bert config json file This MLCommons members Google Drive location contain
For auto aligning, cropping, and scaling HR and LR images for training image based neural networks
ImgAlign For auto aligning, cropping, and scaling HR and LR images for training image based neural networks Usage Make sure OpenCV is installed, 'pip
NAS-HPO-Bench-II is the first benchmark dataset for joint optimization of CNN and training HPs.
NAS-HPO-Bench-II API Overview NAS-HPO-Bench-II is the first benchmark dataset for joint optimization of CNN and training HPs. It helps a fair and low-
Improving Non-autoregressive Generation with Mixup Training
MIST Training MIST TRAIN_FILE=/your/path/to/train.json VALID_FILE=/your/path/to/valid.json OUTPUT_DIR=/your/path/to/save_checkpoints CACHE_DIR=/your/p
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
Code release for "Cycle Self-Training for Domain Adaptation" (NeurIPS 2021)
CST Code release for "Cycle Self-Training for Domain Adaptation" (NeurIPS 2021) Prerequisites torch=1.7.0 torchvision qpsolvers numpy prettytable tqd
A library for preparing, training, and evaluating scalable deep learning hybrid recommender systems using PyTorch.
collie Collie is a library for preparing, training, and evaluating implicit deep learning hybrid recommender systems, named after the Border Collie do
QuakeLabeler is a Python package to create and manage your seismic training data, processes, and visualization in a single place — so you can focus on building the next big thing.
QuakeLabeler Quake Labeler was born from the need for seismologists and developers who are not AI specialists to easily, quickly, and independently bu
🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥
face.evoLVe: High-Performance Face Recognition Library based on PaddlePaddle & PyTorch Evolve to be more comprehensive, effective and efficient for fa
The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
PRIMER The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization. PRIMER is a pre-trained model for mu
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 "
A Deep Learning based project for creating line art portraits.
ArtLine The main aim of the project is to create amazing line art portraits. Sounds Intresting,let's get to the pictures!! Model-(Smooth) Model-(Quali
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more
PyTorch Image Models Sponsors What's New Introduction Models Features Results Getting Started (Documentation) Train, Validation, Inference Scripts Awe
Synthetic structured data generators
Join us on What is Synthetic Data? Synthetic data is artificially generated data that is not collected from real world events. It replicates the stati
Source code for the paper "PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction" in ACL2021
PLOME:Pre-training with Misspelled Knowledge for Chinese Spelling Correction (ACL2021) This repository provides the code and data of the work in ACL20
PaSST: Efficient Training of Audio Transformers with Patchout
PaSST: Efficient Training of Audio Transformers with Patchout This is the implementation for Efficient Training of Audio Transformers with Patchout Pa
Codebase for Time-series Generative Adversarial Networks (TimeGAN)
Codebase for Time-series Generative Adversarial Networks (TimeGAN)
Scalable training for dense retrieval models.
Scalable implementation of dense retrieval. Training on cluster By default it trains locally: PYTHONPATH=.:$PYTHONPATH python dpr_scale/main.py traine
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
NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in production.
NVIDIA Merlin NVIDIA Merlin is an open source library designed to accelerate recommender systems on NVIDIA’s GPUs. It enables data scientists, machine
The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
PRIMER The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization. PRIMER is a pre-trained model for mu
MG-GCN: Scalable Multi-GPU GCN Training Framework
MG-GCN MG-GCN: multi-GPU GCN training framework. For more information, please read our paper. After cloning our repository, run git submodule update -
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
Demystifying How Self-Supervised Features Improve Training from Noisy Labels
Demystifying How Self-Supervised Features Improve Training from Noisy Labels This code is a PyTorch implementation of the paper "[Demystifying How Sel
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.
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System Authors: Yixuan Su, Lei Shu, Elman Mansimov, Arshit Gupta, Deng Cai, Yi-An Lai
🦙 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
Codes for CIKM'21 paper 'Self-Supervised Graph Co-Training for Session-based Recommendation'.
COTREC Codes for CIKM'21 paper 'Self-Supervised Graph Co-Training for Session-based Recommendation'. Requirements: Python 3.7, Pytorch 1.6.0 Best Hype
[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
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution (ICCV, 2021) (PyTorch) - We released the training code!
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution Kai Zhang, Jingyun Liang, Luc Van Gool, Radu Timofte Computer Vision Lab
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
Code associated with the paper "Towards Understanding the Data Dependency of Mixup-style Training".
Mixup-Data-Dependency Code associated with the paper "Towards Understanding the Data Dependency of Mixup-style Training". Running Alternating Line Exp
Anomaly detection in multi-agent trajectories: Code for training, evaluation and the OpenAI highway simulation.
Anomaly Detection in Multi-Agent Trajectories for Automated Driving This is the official project page including the paper, code, simulation, baseline
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
collect training and calibration data for gaze tracking
Collect Training and Calibration Data for Gaze Tracking This tool allows collecting gaze data necessary for personal calibration or training of eye-tr
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
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System Authors: Yixuan Su, Lei Shu, Elman Mansimov, Arshit Gupta, Deng Cai, Yi-An Lai
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
Propose a principled and practically effective framework for unsupervised accuracy estimation and error detection tasks with theoretical analysis and state-of-the-art performance.
Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles This project is for the paper: Detecting Errors and Estimating
An Agnostic Computer Vision Framework - Pluggable to any Training Library: Fastai, Pytorch-Lightning with more to come
IceVision is the first agnostic computer vision framework to offer a curated collection with hundreds of high-quality pre-trained models from torchvision, MMLabs, and soon Pytorch Image Models. It orchestrates the end-to-end deep learning workflow allowing to train networks with easy-to-use robust high-performance libraries such as Pytorch-Lightning and Fastai
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
Composing methods for ML training efficiency
MosaicML Composer contains a library of methods, and ways to compose them together for more efficient ML training.
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
This is a library for training and applying sparse fine-tunings with torch and transformers.
This is a library for training and applying sparse fine-tunings with torch and transformers. Please refer to our paper Composable Sparse Fine-Tuning f
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
ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The training codes are in BasicSR.
ESRGAN (Enhanced SRGAN) [ 🚀 BasicSR] [Real-ESRGAN] ✨ New Updates. We have extended ESRGAN to Real-ESRGAN, which is a more practical algorithm for rea
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks This repository contains a TensorFlow implementation of "
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
PyTorch implementation of paper “Unbiased Scene Graph Generation from Biased Training”
A new codebase for popular Scene Graph Generation methods (2020). Visualization & Scene Graph Extraction on custom images/datasets are provided. It's also a PyTorch implementation of paper “Unbiased Scene Graph Generation from Biased Training CVPR 2020”
Code for reproducing experiments in "Improved Training of Wasserstein GANs"
Improved Training of Wasserstein GANs Code for reproducing experiments in "Improved Training of Wasserstein GANs". Prerequisites Python, NumPy, Tensor
Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm
DeCLIP Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm. Our paper is available in arxiv Updates ** Ou
Self-Supervised Speech Pre-training and Representation Learning Toolkit.
What's New Sep 2021: We host a challenge in AAAI workshop: The 2nd Self-supervised Learning for Audio and Speech Processing! See SUPERB official site
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
Explaining in Style: Training a GAN to explain a classifier in StyleSpace
Explaining in Style: Official TensorFlow Colab Explaining in Style: Training a GAN to explain a classifier in StyleSpace Oran Lang, Yossi Gandelsman,
MosaicML Composer contains a library of methods, and ways to compose them together for more efficient ML training
MosaicML Composer MosaicML Composer contains a library of methods, and ways to compose them together for more efficient ML training. We aim to ease th
Calibrate your listeners! Robust communication-based training for pragmatic speakers. Findings of EMNLP 2021.
Calibrate your listeners! Robust communication-based training for pragmatic speakers Rose E. Wang, Julia White, Jesse Mu, Noah D. Goodman Findings of
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
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.
Alias-Free Generative Adversarial Networks (StyleGAN3) Official PyTorch implementation
Alias-Free Generative Adversarial Networks (StyleGAN3) Official PyTorch implementation
Code for the paper Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations (AKBC 2021).
Relation Prediction as an Auxiliary Training Objective for Knowledge Base Completion This repo provides the code for the paper Relation Prediction as
Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR
UniSpeech The family of UniSpeech: UniSpeech (ICML 2021): Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR UniSpeech-
Pre-training BERT masked language models with custom vocabulary
Pre-training BERT Masked Language Models (MLM) This repository contains the method to pre-train a BERT model using custom vocabulary. It was used to p
Unofficial PyTorch implementation of DeepMind's Perceiver IO with PyTorch Lightning scripts for distributed training
Unofficial PyTorch implementation of DeepMind's Perceiver IO with PyTorch Lightning scripts for distributed training
ICLR 2021: Pre-Training for Context Representation in Conversational Semantic Parsing
SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing This repository contains code for the ICLR 2021 paper "SCoRE: Pre-Tr
Training vision models with full-batch gradient descent and regularization
Stochastic Training is Not Necessary for Generalization -- Training competitive vision models without stochasticity This repository implements trainin
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
Training RNNs as Fast as CNNs
News SRU++, a new SRU variant, is released. [tech report] [blog] The experimental code and SRU++ implementation are available on the dev branch which
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.
A simple recipe for training and inferencing Transformer architecture for Multi-Task Learning on custom datasets. You can find two approaches for achieving this in this repo.
multitask-learning-transformers A simple recipe for training and inferencing Transformer architecture for Multi-Task Learning on custom datasets. You
🧬 Training the car to do self-parking using a genetic algorithm
🧬 Training the car to do self-parking using a genetic algorithm