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Metric Learning

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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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Metric Learning

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Why and How: Knowledge-Guided Learning for Cross-Spectral Image Patch Matching

Unsupervised Ground Metric Learning

Are encoders able to learn landmarkers for warm-starting of Hyperparameter Optimization?

$\texttt{Droid}$: A Resource Suite for AI-Generated Code Detection

Grid-Reg: Grid-Based SAR and Optical Image Registration Across Platforms

Dare to Plagiarize? Plagiarized Painting Recognition and Retrieval

Multimodal Information Retrieval for Open World with Edit Distance Weak Supervision

Offline Goal-Conditioned Reinforcement Learning with Projective Quasimetric Planning

AbRank: A Benchmark Dataset and Metric-Learning Framework for Antibody-Antigen Affinity Ranking

Global Ground Metric Learning with Applications to scRNA data

Few-Shot Learning for Industrial Time Series: A Comparative Analysis Using the Example of Screw-Fastening Process Monitoring

Discrete Scale-invariant Metric Learning for Efficient Collaborative Filtering

Synthetic Speech Source Tracing using Metric Learning

Self-supervised Latent Space Optimization with Nebula Variational Coding

Unveiling Audio Deepfake Origins: A Deep Metric learning And Conformer Network Approach With Ensemble Fusion

DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers

Emotion-aware Dual Cross-Attentive Neural Network with Label Fusion for Stance Detection in Misinformative Social Media Content

Learning to Select In-Context Demonstration Preferred by Large Language Model

Adversarial Deep Metric Learning for Cross-Modal Audio-Text Alignment in Open-Vocabulary Keyword Spotting

LLM-based Evaluation Policy Extraction for Ecological Modeling

Negative Metric Learning for Graphs

Physics-informed Temporal Difference Metric Learning for Robot Motion Planning

Let Humanoids Hike! Integrative Skill Development on Complex Trails

GRAML: Goal Recognition As Metric Learning

ProFi-Net: Prototype-based Feature Attention with Curriculum Augmentation for WiFi-based Gesture Recognition

A Few-Shot Metric Learning Method with Dual-Channel Attention for Cross-Modal Same-Neuron Identification

Proxy-Anchor and EVT-Driven Continual Learning Method for Generalized Category Discovery

Optimistic Online Learning in Symmetric Cone Games

Randomized Pairwise Learning with Adaptive Sampling: A PAC-Bayes Analysis

Reasoning and Learning a Perceptual Metric for Self-Training of Reflective Objects in Bin-Picking with a Low-cost Camera

Unlocking the Hidden Potential of CLIP in Generalizable Deepfake Detection

Enhancing Martian Terrain Recognition with Deep Constrained Clustering

Reconstructing Cell Lineage Trees from Phenotypic Features with Metric Learning

Riemannian Metric Learning: Closer to You than You Imagine

Radar Pulse Deinterleaving with Transformer Based Deep Metric Learning

Stability-based Generalization Analysis of Randomized Coordinate Descent for Pairwise Learning