Traditionally, they have defined metrics in a variety of ways, including Euclidean distance and cosine similarity.
💡I hope that many people will learn about metric learning through this repository.
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Dimensionality Reduction by Learning an Invariant Mapping (Contrastive) (CVPR 2006) [Paper][Caffe][Tensorflow][Keras][Pytorch1][Pytorch2]
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FaceNet: A Unified Embedding for Face Recognition and Clustering (Triplet) (CVPR 2015) [Paper][Tensorflow][Pytorch]
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Deep Metric Learning Using Triplet Network (ICLR 2015 workshop) [Paper][Keras][Torch]
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Regressive Virtual Metric Learning (NIPS 2015) [Paper] (Strictly speaking, they used Mahalanobis distance)
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Deep Metric Learning via Lifted Structured Feature Embedding (LSSS) (CVPR 2016) [Paper][Chainer][Caffe][Pytorch1][Pytorch2][Tensorflow]
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Improved Deep Metric Learning with Multi-class N-pair Loss Objective (N-pair) (NIPS 2016) [Paper][Pytorch][Chainer]
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Beyond triplet loss: a deep quadruplet network for person re-identification (Quadruplet) (CVPR 2017) [Paper]
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Deep Metric Learning via Facility Location (CVPR 2017) [Paper][Tensorflow]
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No Fuss Distance Metric Learning using Proxies (Proxy NCA) (ICCV 2017) [Paper][Pytorch1][Pytorch2][Chainer]
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Deep Metric Learning with Angular Loss (CVPR 2017) [Paper][Tensorflow][Chainer]
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Ranked List Loss for Deep Metric Learning (CVPR 2019) [Paper]
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Hardness-Aware Deep Metric Learning (CVPR 2019) [Paper][Tensorflow]
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Deep Metric Learning for Practical Person Re-Identification [Paper][Tensorflow][Pytorch]
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Learning Deep Embeddings with Histogram Loss (NIPS 2016) [Paper][Tensorflow][Pytorch][Caffe]
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Learning Deep Disentangled Embeddings With the F-Statistic Loss (NIPS 2018) [Paper][Tensorflow]
- Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning (NIPS 2018) [Paper][Tensorflow]
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BIER-Boosting Independent Embeddings Robustly (ICCV 2017) [Paper][Tensorflow]
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Hard-Aware Deeply Cascaded Embedding (ICCV 2017) [Paper][Caffe]
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Deep Adversarial Metric Learning (CVPR 2018) [Paper][Chainer]
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Deep Randomized Ensembles for Metric Learning (ECCV 2018) [Paper][Pytorch]
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Attention-based Ensemble for Deep Metric Learning (ECCV 2018) [Paper]
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Deep Metric Learning with Hierarchical Triplet Loss (ECCV 2018) [Paper]
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Distance Metric Learning for Large Margin Nearest Neighbor Classification (NIPS 2005) [Paper][Journal]
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Metric Learning by Collapsing Classes (NIPS 2005) [Paper]
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Discriminative Metric Learning by Neighborhood Gerrymandering (NIPS 2014) [Paper]
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Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces (NIPS 2014) [Paper]
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Sample complexity of learning Mahalanobis distance metrics (NIPS 2015) [Paper]
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Improved Error Bounds for Tree Representations of Metric Spaces (NIPS 2016) [Paper]
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What Makes Objects Similar: A Unified Multi-Metric Learning Approach (NIPS 2016) [Paper]
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Learning Low-Dimensional Metrics (NIPS 2017) [Paper]
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Generative Local Metric Learning for Kernel Regression (NIPS 2017) [Paper]
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Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams (NIPS 2018) [Paper]
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Bilevel Distance Metric Learning for Robust Image Recognition (NIPS 2018) [Paper]
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Add Euclidean-based metric
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Add Similarity-based metric
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Add Ensemble-based metric
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Add application web sites
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Add brief descriptions