zjfheart

zjfheart's Stars

• Stability-AI/stablediffusion

  High-Resolution Image Synthesis with Latent Diffusion Models

  Language:Python39k4443125k

• chenfei-wu/TaskMatrix

  Language:Python34.5k3003543.3k

• huggingface/pytorch-image-models

  The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

  Language:Python32.1k3129214.7k

• Vision-CAIR/MiniGPT-4

  Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)

  Language:Python25.4k2184612.9k

• hehonghui/awesome-english-ebooks

  经济学人(含音频)、纽约客、卫报、连线、大西洋月刊等英语杂志免费下载,支持epub、mobi、pdf格式, 每周更新

  Language:CSS21.9k57001.7k

• openai/baselines

  OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

  Language:Python15.8k6478504.9k

• AIGC-Audio/AudioGPT

  AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head

  Language:Python10k13650862

• breezedeus/CnOCR

  CnOCR: Awesome Chinese/English OCR Python toolkits based on PyTorch. It comes with 20+ well-trained models for different application scenarios and can be used directly after installation. 【基于 PyTorch/MXNet 的中文/英文 OCR Python 包。】

  Language:Python3.2k67240504

• facebookresearch/jepa

  PyTorch code and models for V-JEPA self-supervised learning from video.

  Language:Python2.7k3652255

• zjfheart/Friendly-Adversarial-Training

  Attacks Which Do Not Kill Training Make Adversarial Learning Stronger (ICML2020 Paper)

  Language:Python1245330

• keqingrong/system-fonts

  Which fonts can I use?

  Language:JavaScript69213

• AlanPeng0897/Defend_MI

  Bilateral Dependency Optimization: Defending Against Model-inversion Attacks

  Language:Python21175

• HanshuYAN/CIFS

  CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection

  Language:Python20102

• ZFancy/IAD

  [ICLR 2022] Reliable Adversarial Distillation with Unreliable Teachers

  Language:Python20213

• Sjtubrian/SAMMD

  This is the source code for Maximum Mean Discrepancy Test is Aware of Adversarial Attacks (ICML2021).

  Language:Python195

• GodXuxilie/RobustSSL_Benchmark

  Benchmark of robust self-supervised learning (RobustSSL) methods & Code for AutoLoRa (ICLR 2024).

  Language:Python15424

• RoyalSkye/ATCL

  [NeurIPS 2022] "Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks"

  Language:Python13203

• zjfheart/BadLabels

  Challenging label noise called BadLabel; Robust label-noise learning called Robust DivideMix

  Language:Python9210

• zjfheart/NoiLIn

  The code for NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels (TMLR 22 accept)

  Language:Python9302

• GodXuxilie/Efficient_ACL_via_RCS

  Efficient Adversarial Contrastive Learning via Robustness-aware Coreset Selection (NeurIPS 2023 Spotlight)

  Language:Python8301

• GodXuxilie/Enhancing_ACL_via_AIR

  Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization (NeurIPS 2023)

  Language:Python8212

• WilliamLUO0/StablePrivateLoRA

  Language:Python810

• d12306/dsnet

  Code for ICML'21 paper Learning Diverse-Structured Networks for Adversarial Robustness

  Language:Python6112

• HanshuYAN/ObsAtk

  Implementation of "Towards Adversarially Robust Deep Image Denoising" (IJCAI 2022)

  Language:Python6100

• yamizi/taskaugment

  Language:Python6102

• trustMLresearch/trustMLresearch.github.io

  Website

  Language:HTML5500

• GodXuxilie/Robust-TST

  Adversarial Attacks and Defense for Non-Parametric Two-Sample Tests (ICML 2022)

  Language:Python420

• zjfheart/Poison-adv-training

  Poisoning attack methods against adversarial training algorithms

  Language:Python3100

• wsl-workshop/wsl-workshop.github.io

  Weakly supervised learning workshops

  Language:HTML2800

• cuis15/synergy-of-experts

  Language:Python1100