Accelerated DL R&D
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Updated
Aug 10, 2020 - Python
Accelerated DL R&D
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
Open source person re-identification library in python
Metric learning algorithms in Python
Code for the NIPS 2017 Paper "Prototypical Networks for Few-shot Learning"
This is the implementation of paper <Additive Margin Softmax for Face Verification>
A simple yet effective loss function for face verification.
PyTorch Implementation for Deep Metric Learning Pipelines
Hardnet descriptor model - "Working hard to know your neighbor's margins: Local descriptor learning loss"
A comprehensive survey of deep metric learning and related works
Official source code of "Batch DropBlock Network for Person Re-identification and Beyond" (ICCV 2019)
Source code for the paper "Divide and Conquer the Embedding Space for Metric Learning", CVPR 2019
PyTorch implementation of a deep metric learning technique called "Magnet Loss" from Facebook AI Research (FAIR) in ICLR 2016.
Official pytorch Implementation of Relational Knowledge Distillation, CVPR 2019
A PyTorch library for benchmarking deep metric learning. It's powerful.
A PyTorch framework for an image retrieval task including implementation of N-pair Loss (NIPS 2016) and Angular Loss (ICCV 2017).
Angular penalty loss functions in Pytorch (ArcFace, SphereFace, Additive Margin, CosFace)
In defence of metric learning for speaker recognition
Deep metric learning methods implemented in Chainer
SegSort: Segmentation by Discriminative Sorting of Segments
Deep Face Recognition in PyTorch
code for ICCV19 paper "Deep Meta Metric Learning"
Code for Supervised Word Mover's Distance (SWMD)
Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval (CVPR 2019)
Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease (AMIA 2018)
(ICML 2020) This repo contains code for our paper "Revisiting Training Strategies and Generalization Performance in Deep Metric Learning" (https://arxiv.org/abs/2002.08473) to facilitate consistent research in this field.
Local Fisher Discriminant Analysis in R
Code the the paper "Metric learning: cross-entropy vs. pairwise losses" (ECCV 2020 - Spotlight)
Code for CVPR 2019 paper "Deep Metric Learning to Rank"
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