Metric Learning

484 papers with code • 8 benchmarks • 31 datasets

The goal of Metric Learning is to learn a representation function that maps objects into an embedded space. The distance in the embedded space should preserve the objects’ similarity — similar objects get close and dissimilar objects get far away. Various loss functions have been developed for Metric Learning. For example, the contrastive loss guides the objects from the same class to be mapped to the same point and those from different classes to be mapped to different points whose distances are larger than a margin. Triplet loss is also popular, which requires the distance between the anchor sample and the positive sample to be smaller than the distance between the anchor sample and the negative sample.

Source: Road Network Metric Learning for Estimated Time of Arrival

Most implemented papers

Associative Alignment for Few-shot Image Classification

ML-Bee/associative-alignment-fs ECCV 2020

Few-shot image classification aims at training a model from only a few examples for each of the "novel" classes.

Ranking and Classification driven Feature Learning for Person Re_identification

Qidian213/Ranked_Person_ReID 25 Dec 2019

However, the method that using Triplet loss as loss function converges slowly, and the method in which pull features of the same class as close as possible in features space leads to poor feature stability.

Distance Metric Learning for Graph Structured Data

takeuchi-lab/Learning-Interpretable-Metric-between-Graphs 3 Feb 2020

Hence, we propose a supervised distance metric learning method for the graph classification problem.

An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality

spitis/deepnorms ICLR 2020

When defining distances, the triangle inequality has proven to be a useful constraint, both theoretically--to prove convergence and optimality guarantees--and empirically--as an inductive bias.

CoLES: Contrastive Learning for Event Sequences with Self-Supervision

dllllb/coles-paper 19 Feb 2020

We address the problem of self-supervised learning on discrete event sequences generated by real-world users.

Generalized Product Quantization Network for Semi-supervised Image Retrieval

youngkyunJang/GPQ CVPR 2020

Image retrieval methods that employ hashing or vector quantization have achieved great success by taking advantage of deep learning.

Embedding Expansion: Augmentation in Embedding Space for Deep Metric Learning

clovaai/embedding-expansion CVPR 2020

Meanwhile, post-processing techniques, such as query expansion and database augmentation, have proposed the combination of feature points to obtain additional semantic information.

OpenGAN: Open Set Generative Adversarial Networks

lukeditria/opengan 18 Mar 2020

Using a state-of-the-art metric learning model that encodes both class-level and fine-grained semantic information, we are able to generate samples that are semantically similar to a given source image.

Maximum Density Divergence for Domain Adaptation

lijin118/ATM 27 Apr 2020

In this paper, we propose a new domain adaptation method named Adversarial Tight Match (ATM) which enjoys the benefits of both adversarial training and metric learning.