👏 Survey of Deep Metric Learning

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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1️⃣ Euclidean-based metric

• Dimensionality Reduction by Learning an Invariant Mapping (Contrastive) (CVPR 2006) [Paper][Caffe][Tensorflow][Keras][Pytorch1][Pytorch2]

• FaceNet: A Unified Embedding for Face Recognition and Clustering (Triplet) (CVPR 2015) [Paper][Tensorflow][Pytorch]

• Deep Metric Learning Using Triplet Network (ICLR 2015 workshop) [Paper][Keras][Torch]

• Regressive Virtual Metric Learning (NIPS 2015) [Paper] (Strictly speaking, they used Mahalanobis distance)

• Deep Metric Learning via Lifted Structured Feature Embedding (LSSS) (CVPR 2016) [Paper][Chainer][Caffe][Pytorch1][Pytorch2][Tensorflow]

• Improved Deep Metric Learning with Multi-class N-pair Loss Objective (N-pair) (NIPS 2016) [Paper][Pytorch][Chainer]

• Beyond triplet loss: a deep quadruplet network for person re-identification (Quadruplet) (CVPR 2017) [Paper]

• Deep Metric Learning via Facility Location (CVPR 2017) [Paper][Tensorflow]

• No Fuss Distance Metric Learning using Proxies (Proxy NCA) (ICCV 2017) [Paper][Pytorch1][Pytorch2][Chainer]

• Deep Metric Learning with Angular Loss (CVPR 2017) [Paper][Tensorflow][Chainer]

• Ranked List Loss for Deep Metric Learning (CVPR 2019) [Paper]

• Hardness-Aware Deep Metric Learning (CVPR 2019) [Paper][Tensorflow]

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2️⃣ Similarity-based metric

• Deep Metric Learning for Practical Person Re-Identification [Paper][Tensorflow][Pytorch]

• Learning Deep Embeddings with Histogram Loss (NIPS 2016) [Paper][Tensorflow][Pytorch][Caffe]

• Learning Deep Disentangled Embeddings With the F-Statistic Loss (NIPS 2018) [Paper][Tensorflow]

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3️⃣ Integrated framework

• Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning (NIPS 2018) [Paper][Tensorflow]

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4️⃣ Ensemble method

• BIER-Boosting Independent Embeddings Robustly (ICCV 2017) [Paper][Tensorflow]

• Hard-Aware Deeply Cascaded Embedding (ICCV 2017) [Paper][Caffe]

• Deep Adversarial Metric Learning (CVPR 2018) [Paper][Chainer]

• Deep Randomized Ensembles for Metric Learning (ECCV 2018) [Paper][Pytorch]

• Attention-based Ensemble for Deep Metric Learning (ECCV 2018) [Paper]

• Deep Metric Learning with Hierarchical Triplet Loss (ECCV 2018) [Paper]

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5️⃣ Related works

• Distance Metric Learning for Large Margin Nearest Neighbor Classification (NIPS 2005) [Paper][Journal]

• Metric Learning by Collapsing Classes (NIPS 2005) [Paper]

• Discriminative Metric Learning by Neighborhood Gerrymandering (NIPS 2014) [Paper]

• Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces (NIPS 2014) [Paper]

• Sample complexity of learning Mahalanobis distance metrics (NIPS 2015) [Paper]

• Improved Error Bounds for Tree Representations of Metric Spaces (NIPS 2016) [Paper]

• What Makes Objects Similar: A Unified Multi-Metric Learning Approach (NIPS 2016) [Paper]

• Learning Low-Dimensional Metrics (NIPS 2017) [Paper]

• Generative Local Metric Learning for Kernel Regression (NIPS 2017) [Paper]

• Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams (NIPS 2018) [Paper]

• Bilevel Distance Metric Learning for Robust Image Recognition (NIPS 2018) [Paper]

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Milestone

• Add Euclidean-based metric

• Add Similarity-based metric

• Add Ensemble-based metric

• Add application web sites

• Add brief descriptions