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Search results for image retrieval deep metric learning
deep-metric-learning
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image-retrieval
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14 search results found
Pytorch Metric Learning
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5,734
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
Deep_metric
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683
Deep Metric Learning
Proxy Anchor Cvpr2020
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259
Official PyTorch Implementation of Proxy Anchor Loss for Deep Metric Learning, CVPR 2020
Revisiting_deep_metric_learning_pytorch
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217
(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.
Pytorch Image Retrieval
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147
A PyTorch framework for an image retrieval task including implementation of N-pair Loss (NIPS 2016) and Angular Loss (ICCV 2017).
Deepembeding
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54
图像检索和向量搜索,similarity learning,compare deep metric and deep-hashing applying in image retrieval
Ranked List Loss For Dml
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41
CVPR 2019: Ranked List Loss for Deep Metric Learning, with extension for TPAMI submission
Tf Metric Learning
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28
Minimalistic TensorFlow2+ deep metric/similarity learning library with loss functions, miners, and utils as embedding projector.
Labelrelaxation Cvpr21
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27
Official PyTorch Implementation of Embedding Transfer with Label Relaxation for Improved Metric Learning, CVPR 2021
Eccv2020_diva_multifeature_dml
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26
(ECCV 2020) This repo contains code for "DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning" (https://arxiv.org/abs/2004.13458), which extends vanilla DML with auxiliary and self-supervised features.
Proxy Synthesis
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20
Official PyTorch implementation of "Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning" (AAAI 2021)
S2sd
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14
Implementation for S2SD - Simultaneous Similarity-based Self-Distillation for Deep Metric Learning
Aaai2021_mdr
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10
Official Code of AAAI 2021 Paper "Multi-level Distance Regularization for Deep Metric Learning"
Autolr
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5
AutoLR: Layer-wise Pruning and Auto-tuning of Learning Rates in Fine-tuning of Deep Networks
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