Stars
[NeurIPS 2020] “ Robust Pre-Training by Adversarial Contrastive Learning”, Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang
Benchmark of robust self-supervised learning (RobustSSL) methods & Code for AutoLoRa (ICLR 2024).
❄️🔥 Visual Prompt Tuning [ECCV 2022] https://arxiv.org/abs/2203.12119
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs
Traffic Sign Recognition - Fine tuning VGG16 + GTSRB
Pytorch code for the IEEE TM/ECCV paper "Attend and rectify"
Attention Branch Network (CIFAR100, ImageNet models)
Paper sharing in adversary related works
Tensorflow implementation of Meta Adversarial Training for Adversarial Patch Attacks on Tiny ImageNet.
Learn how to use CARLA with basic APIs
a Pytorch implementation of the paper "Generating Adversarial Examples with Adversarial Networks" (advGAN).
This repository implements several swarm optimization algorithms and visualizes them. Implemented algorithms: Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Cuckoo Search (CS), Ant Colo…
A dependency free library of standardized optimization test functions written in pure Python.
Minimal PyTorch implementation of YOLOv3
Experiments on meta-learning algorithms to solve few-shot domain adaptation
PGADA Official Repo. (Best Student Paper Award, PAKDD 22)
My implementation of Few-Shot Adversarial Learning of Realistic Neural Talking Head Models (Egor Zakharov et al.).
[IJCAI 2021 & AIJ 2023] Cross-Domain Few-Shot Classification via Adversarial Task Augmentation
A PyTorch implementation of the method found in "Adversarially Robust Few-Shot Learning: A Meta-Learning Approach"
[ICLR 2020] A repository for extremely fast adversarial training using FGSM
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
RobustBench: a standardized adversarial robustness benchmark [NeurIPS'21 Benchmarks and Datasets Track]
Tensorflow 2.0 Notes 提供了TF2.0案例实战以及TF2.0基础实战，目标是帮助那些希望和使用Tensorflow 2.0进行深度学习开发和研究的朋友快速入门，其中包含的Tensorflow 2.0教程基本通过测试保证可以成功运行(有问题的可以提issue，笔记网站正在建设中)。