Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Voxceleb_trainer | 719 | a year ago | 10 | mit | Python | |||||
In defence of metric learning for speaker recognition | ||||||||||
Deep_metric | 683 | 4 years ago | 26 | apache-2.0 | Python | |||||
Deep Metric Learning | ||||||||||
Powerful Benchmarker | 426 | 3 months ago | 34 | September 19, 2020 | 3 | Jupyter Notebook | ||||
A library for ML benchmarking. It's powerful. | ||||||||||
Deep Metric Learning Baselines | 330 | 4 years ago | apache-2.0 | Python | ||||||
PyTorch Implementation for Deep Metric Learning Pipelines | ||||||||||
Fcgf | 304 | 3 years ago | 14 | mit | Python | |||||
Fully Convolutional Geometric Features: Fast and accurate 3D features for registration and correspondence. | ||||||||||
Revisiting_deep_metric_learning_pytorch | 217 | 3 years ago | 3 | mit | Python | |||||
(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 the field of Deep Metric Learning. | ||||||||||
Magnetloss Pytorch | 213 | 6 years ago | 2 | mit | Python | |||||
PyTorch implementation of a deep metric learning technique called "Magnet Loss" from Facebook AI Research (FAIR) in ICLR 2016. | ||||||||||
Metric Learning Divide And Conquer | 213 | 4 years ago | 4 | lgpl-3.0 | Python | |||||
Source code for the paper "Divide and Conquer the Embedding Space for Metric Learning", CVPR 2019 | ||||||||||
Deep_metric_learning | 148 | 6 years ago | 5 | mit | Python | |||||
Deep metric learning methods implemented in Chainer | ||||||||||
Disent | 87 | a year ago | 34 | November 07, 2022 | 14 | mit | Python | |||
🧶 Modular VAE disentanglement framework for python built with PyTorch Lightning ▸ Including metrics and datasets ▸ With strongly supervised, weakly supervised and unsupervised methods ▸ Easily configured and run with Hydra config ▸ Inspired by disentanglement_lib |