mfaraki

mfaraki's Stars

• mlfoundations/wise-ft

  Robust fine-tuning of zero-shot models

  Language:Python64367

• facebookresearch/DomainBed

  DomainBed is a suite to test domain generalization algorithms

  Language:Python1.4k298

• aimagelab/mammoth

  An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning

  Language:Python551101

• HuangYG123/DDL

  Improving Face Recognition from Hard Samplesvia Distribution Distillation Loss

  466

• HuangYG123/CurricularFace

  CurricularFace(CVPR2020)

  Language:Python52972

• KevinMusgrave/pytorch-metric-learning

  The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

  Language:Python6k658

• adambielski/siamese-triplet

  Siamese and triplet networks with online pair/triplet mining in PyTorch

  Language:Python3.1k633

• amber0309/Domain-generalization

  All about domain generalization

  832124

• dragen1860/MAML-Pytorch

  Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)

  Language:Python2.3k420

• neale/Adversarial-Autoencoder

  An adversarial autoencoder implementation in pytorch

  Language:Python8317

• WeiTang114/BlenderPhong

  Language:Python7916

• RBirkeland/MVCNN-PyTorch

  Language:Python10134

• meetps/pytorch-semseg

  Semantic Segmentation Architectures Implemented in PyTorch

  Language:Python3.4k797

• wasidennis/AdaptSegNet

  Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)

  Language:Python849203

• Britefury/self-ensemble-visual-domain-adapt

  Code repository for the small image experiments our paper 'Self-ensembling for Domain Adaptation'

  Language:Python19234

• mil-tokyo/MCD_DA

  Language:Python557153

• fungtion/DANN

  pytorch implementation of Domain-Adversarial Training of Neural Networks

  Language:Python842152

• pierreroudier/clhs

  A R implementation of the conditioned Latin Hypercube Sampling method

  Language:R129