wnn2000

wnn2000's Stars

• pengsida/learning_research

  本人的科研经验

  6k7228357

• tomgoldstein/loss-landscape

  Code for visualizing the loss landscape of neural nets

  Language:Python2.8k3341400

• youngfish42/Awesome-FL

  Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)

  Language:Python1.5k396187

• shaoxiongji/federated-learning

  A PyTorch Implementation of Federated Learning http://doi.org/10.5281/zenodo.4321561

  Language:Python1.3k1528372

• hendrycks/robustness

  Corruption and Perturbation Robustness (ICLR 2019)

  Language:Python1k1455145

• huytransformer/Awesome-Out-Of-Distribution-Detection

  Out-of-distribution detection, robustness, and generalization resources. The repository contains a curated list of papers, tutorials, books, videos, articles and open-source libraries etc

  82837469

• hendrycks/ss-ood

  Self-Supervised Learning for OOD Detection (NeurIPS 2019)

  Language:Python26682531

• kohpangwei/group_DRO

  Distributionally robust neural networks for group shifts

  Language:Python24981449

• juntang-zhuang/GSAM

  PyTorch repository for ICLR 2022 paper (GSAM) which improves generalization (e.g. +3.8% top-1 accuracy on ImageNet with ViT-B/32)

  Language:Python1384715

• ShadeAlsha/LTR-weight-balancing

  CVPR 2022 - official implementation for "Long-Tailed Recognition via Weight Balancing" https://arxiv.org/abs/2203.14197

  Language:Jupyter Notebook12331710

• WenkeHuang/RethinkFL

  CVPR2023 - Rethinking Federated Learning with Domain Shift: A Prototype View

  Language:Python931413

• Wang-pengfei/SAGM

  The official codes of our CVPR-2023 paper: Sharpness-Aware Gradient Matching for Domain Generalization

  Language:Python67135

• krishnap25/geom_median

  Fast and differentiable geometric median, a multivariate median analogue. Install with `pip install geom-median`

  Language:Python44209

• cool-xuan/Imbalanced_SAM

  The offical implement of ImbSAM (Imbalanced-SAM)

  Language:Python21222

• JiayiChen815/FEAL

  [CVPR2024] Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts

  Language:Python21122

• SMILELab-FL/FedNoisy

  Benchmark for federated noisy label learning

  Language:Python19105

• AICONSlab/roodmri

  AICONSlab's DL benchmarking platform to OOD data in MRI

  Language:Python17202

• szbonaldo/FedMLP

  The official implementation for the paper: "FedMLP: Federated Multi-Label Medical Image Classiffcation under Task Heterogeneity", which is accepted at MICCAI'24 (Early Accept, top 11% in total 2869 submissions).

  Language:Python17102

• Wakings/TABASCO

  Label-Noise Learning with Intrinsically Long-Tailed Data（ICCV2023）

  Language:Python16131

• med-air/Client-DP-FL

  [MICCAI2023] Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical Imaging

  Language:Python13112

• wnn2000/FedAAAI

  Official implementation of the paper "FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise"

  Language:Python12200

• zhuohuangai/SharpDRO

  Code for CVPR 2023 Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

  Language:Python11112

• MediaBrain-SJTU/FedGELA

  [NeurIPS 2023]Federated Learning with Bilateral Curation for Partially Class-Disjoint Data

  Language:Python10100

• Sprinter1999/FedELC

  Official codes for ACM CIKM '24 full paper: Tackling Noisy Clients in Federated Learning with End-to-end Label Correction

  Language:Python10100

• glhr/ood-labelnoise

  "A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?" (CVPR 2024)

  Language:Jupyter Notebook8100

• IntelligentInteractiveLab/IntelligentInteractiveLab_old

  智能交互实验室主页

  Language:HTML7107

• wnn2000/FFL4MIA

  Official implementation of the paper "From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching"

  Language:Jupyter Notebook5110

• mahf93/Hard-vs-Noisy

  Code for "Differences between hard and noisy-labeled samples: An empirical study"

  Language:Python3110

• hustdmin/hustdmin.github.io

  Language:JavaScript110

• wnn2000/wnn2000.github.io

  Language:HTML1100