XingzhiZhou

XingzhiZhou's Stars

• jindongwang/transferlearning

  Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

  Language:Python13.5k3393393.8k

• salesforce/LAVIS

  LAVIS - A One-stop Library for Language-Vision Intelligence

  Language:Jupyter Notebook9.9k99667973

• activeloopai/deeplake

  Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai

  Language:Python8.2k90465629

• bethgelab/foolbox

  A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX

  Language:Python2.8k47371426

• lihongxun945/gobang

  javascript gobang AI，JS五子棋AI，源码+教程，基于Alpha-Beta剪枝算法（不是神经网络）

  Language:JavaScript1.7k3655379

• facebookresearch/DomainBed

  DomainBed is a suite to test domain generalization algorithms

  Language:Python1.4k29106299

• KaiyangZhou/Dassl.pytorch

  A PyTorch toolbox for domain generalization, domain adaptation and semi-supervised learning.

  Language:Python1.2k1757171

• DmitryRyumin/ICCV-2023-Papers

  ICCV 2023 Papers: Discover cutting-edge research from ICCV 2023, the leading computer vision conference. Stay updated on the latest in computer vision and deep learning, with code included. ⭐ support visual intelligence development!

  Language:Python935141042

• Jingkang50/OpenOOD

  Benchmarking Generalized Out-of-Distribution Detection

  Language:Python8709109111

• tim-learn/awesome-test-time-adaptation

  Collection of awesome test-time (domain/batch/instance) adaptation methods

  75419151

• RobustBench/robustbench

  RobustBench: a standardized adversarial robustness benchmark [NeurIPS 2021 Benchmarks and Datasets Track]

  Language:Python668911199

• lucidrains/segformer-pytorch

  Implementation of Segformer, Attention + MLP neural network for segmentation, in Pytorch

  Language:Python34391343

• ENSTA-U2IS-AI/torch-uncertainty

  Open-source framework for uncertainty and deep learning models in PyTorch :seedling:

  Language:Python30493720

• changwxx/Awesome-Optimal-Transport-in-Deep-Learning

  a collection of AWESOME things about Optimal Transport in Deep Learning

  1984018

• mariodoebler/test-time-adaptation

  A repository and benchmark for online test-time adaptation.

  Language:Python18831916

• ldkong1205/TranSVAE

  [NeurIPS 2023] Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective

  Language:Jupyter Notebook12291411

• LINs-lab/ttab

  [ICML23] On Pitfalls of Test-Time Adaptation

  Language:Python1032159

• xxgege/GAM

  The official repo for CVPR2023 highlight paper "Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization".

  Language:Python77264

• mzhaoshuai/RLCF

  [ICLR 2024] Test-Time Adaptation with CLIP Reward for Zero-Shot Generalization in Vision-Language Models.

  Language:Python55351

• YutingLi0606/SURE

  “SURE: SUrvey REcipes for building reliable and robust deep networks” (CVPR 2024) & (ECCV 2024 OOD-CV Challenge Winner)

  Language:Python52504

• yuanyige/tea

  [CVPR 2024] TEA: Test-time Energy Adaptation

  Language:Python51221

• MarcBotet/hamlet

  Source code for "To Adapt or Not to Adapt? Real-Time Adaptation for Semantic Segmentation", ICCV 2023

  Language:Python48598

• Yushu-Li/OWTTT

  [ICCV 2023 Oral] Official repository for “On the Robustness of Open-World Test-Time Training: Self-Training with Dynamic Prototype Expansion”

  Language:Python41323

• daeunni/BECoTTA

  Code for "BECoTTA: Input-dependent Online Blending of Experts for Continual Test-time Adaptation [ICML2024]".

  Language:Python37150

• NiFangBaAGe/DATTT

  [CVPR 2024] Depth-aware Test-Time Training for Zero-shot Video Object Segmentation

  Language:Python25422

• Anonymous-012/SVDP

  [AAAI 2024] SVDP: Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction

  Language:Python23650

• taeckyung/SoTTA

  This is the official PyTorch Implementation of "SoTTA: Robust Test-Time Adaptation on Noisy Data Streams (NeurIPS '23)" by Taesik Gong*, Yewon Kim*, Taeckyung Lee*, Sorn Chottananurak, and Sung-Ju Lee (* Equal contribution).

  Language:Python18342

• KunyuLin/STDN

  Language:Python14100

• Jhyun17/DeYO

  Language:Python13123

• devavratTomar/unmixtns

  Un-mixing Test-time Normalization Statistics: Combatting Label Temporal Correlation

  Language:Python6100