About

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

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Datasets

Latest papers without code

End-to-end One-shot Human Parsing

4 May 2021

Previous human parsing models are limited to parsing humans into pre-defined classes, which is inflexible for applications that need to handle new classes.

HUMAN PARSING METRIC LEARNING ONE-SHOT SEGMENTATION

Subspace Representation Learning for Few-shot Image Classification

2 May 2021

In this paper, we propose a subspace representation learning (SRL) framework to tackle few-shot image classification tasks.

CLASSIFICATION FEW-SHOT IMAGE CLASSIFICATION METRIC LEARNING REPRESENTATION LEARNING

VeriMedi: Pill Identification using Proxy-based Deep Metric Learning and Exact Solution

22 Apr 2021

After that, the segmented pill images are sent to the identification solution where a Deep Metric Learning model that is trained with Proxy Anchor Loss (PAL) function generates embedding vectors for each pill image.

CONTINUAL LEARNING FINE-GRAINED VISUAL CATEGORIZATION METRIC LEARNING

Deep Transform and Metric Learning Networks

21 Apr 2021

Based on its great successes in inference and denosing tasks, Dictionary Learning (DL) and its related sparse optimization formulations have garnered a lot of research interest.

DICTIONARY LEARNING METRIC LEARNING

Eye Know You: Metric Learning for End-to-end Biometric Authentication Using Eye Movements from a Longitudinal Dataset

21 Apr 2021

While numerous studies have explored eye movement biometrics since the modality's inception in 2004, the permanence of eye movements remains largely unexplored as most studies utilize datasets collected within a short time frame.

METRIC LEARNING

SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypes

19 Apr 2021

To this end, we generate self-contrastive background prototypes directly from the query image, with which we enable the construction of complete sample pairs and thus a complementary and auxiliary segmentation task to achieve the training of a better segmentation model.

FEW-SHOT SEMANTIC SEGMENTATION METRIC LEARNING SEMANTIC SEGMENTATION

Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations

17 Apr 2021

Furthermore, we combine the dual learning method with the metric learning approach, which allows us to significantly reduce the required common user overlap across the two domains and leads to even better cross-domain recommendation performance.

METRIC LEARNING RECOMMENDATION SYSTEMS

Sparse online relative similarity learning

15 Apr 2021

This is clearly inefficient for high dimensional tasks due to its high memory and computational complexity.

METRIC LEARNING

Deep Evaluation Metric: Learning to Evaluate Simulated Radar Point Clouds for Virtual Testing of Autonomous Driving

14 Apr 2021

The usage of environment sensor models for virtual testing is a promising approach to reduce the testing effort of autonomous driving.

AUTONOMOUS DRIVING METRIC LEARNING

Reducing Representation Drift in Online Continual Learning

11 Apr 2021

We study the online continual learning paradigm, where agents must learn from a changing distribution with constrained memory and compute.

CONTINUAL LEARNING METRIC LEARNING