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ares

A Python library for adversarial machine learning focusing on benchmarking adversarial robustness.

attack-imagenet

No.3 solution of Tianchi ImageNet Adversarial Attack Challenge.

awesome-courses

:books: List of awesome university courses for learning Computer Science!

awesome-deeplearning-resources

Deep Learning and deep reinforcement learning research papers and some codes

cagrad

cv-arxiv-daily

🎓Automatically Update CV Papers Daily using Github Actions (Update Every 12th hours)

equationliu

equationliu.github.io

my blog

examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

faiss

A library for efficient similarity search and clustering of dense vectors.

ga-attack

SaTML 2023, 1st place in CVPR’21 Security AI Challenger: Unrestricted Adversarial Attacks on ImageNet.

gpytorch

A highly efficient and modular implementation of Gaussian Processes in PyTorch

kangaroo

Implementation of Kangaroo: Lossless Self-Speculative Decoding via Double Early Exiting

layercnn

moco

Unofficial implementation with pytorch DistributedDataParallel for "MoCo: Momentum Contrast for Unsupervised Visual Representation Learning"

monodepth-dev

Monocular depth estimation by segmentation models for NYU-depth v2 dataset. Simple PyTorch Lightning implementation.

pkuautosubmit

PKU一键出入校备案小工具

pkuautosubmit_online

萌新友好的，无需下载文件与配环境的，基于Github Actions的，P大学生出入校自动报备程序

python-spider

:rainbow:Python3网络爬虫实战：VIP视频破解助手；GEETEST验证码破解；小说、动漫下载；手机APP爬取；财务报表入库；火车票抢票；抖音APP视频下载；百万英雄辅助；网易云音乐下载；B站视频和弹幕下载；京东晒单图下载

rethinktinylm

The official implementation of “Rethinking Optimization and Architecture for Tiny Language Models”

robust-finetuning

Code relative to "Adversarial robustness against multiple $l_p$-threat models at the price of one and how to quickly fine-tune robust models to another threat model"

robust-vision-transformer

This is the implementation of our paper Towards Robust Vision Transformer

sact

Supervised Adversarial Contrastive Training

self-adaptive-training

Official implementation of the NeurIPS'2020 paper 'Self-Adaptive Training: beyond Empirical Risk Minimization'

smoothing

Provable adversarial robustness at ImageNet scale

smoothing-adversarial

Code for our NeurIPS 2019 paper "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"

trades

TRADES (TRadeoff-inspired Adversarial DEfense via Surrogate-loss minimization)