SOTAVerified

Metric Learning

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 601–650 of 1648 papers

TitleStatusHype
MergedNET: A simple approach for one-shot learning in siamese networks based on similarity layersCode0
A Lower Bound of Hash Codes' PerformanceCode0
Coded Residual Transform for Generalizable Deep Metric Learning—0
Learning to embed semantic similarity for joint image-text retrieval—0
Matching Text and Audio Embeddings: Exploring Transfer-learning Strategies for Language-based Audio Retrieval—0
Fully Unsupervised Training of Few-shot Keyword Spotting—0
Regression-Based Elastic Metric Learning on Shape Spaces of Elastic CurvesCode0
On Background Bias in Deep Metric LearningCode0
Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-IdentificationCode0
Lexical semantics enhanced neural word embeddings—0
And what if two musical versions don't share melody, harmony, rhythm, or lyrics ?—0
Incorporating the Rhetoric of Scientific Language into Sentence Embeddings using Phrase-guided Distant Supervision and Metric LearningCode0
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm—0
Uncertainty Estimation for 3D Dense Prediction via Cross-Point EmbeddingsCode0
A Simple Way to Learn Metrics Between Attributed Graphs—0
Understanding Open-Set Recognition by Jacobian Norm and Inter-Class Separation—0
Query-Guided Networks for Few-shot Fine-grained Classification and Person Search—0
DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation—0
Deep Metric Learning with Chance ConstraintsCode0
Magnetic Resonance Fingerprinting with compressed sensing and distance metric learning—0
Towards A Unified Policy Abstraction Theory and Representation Learning Approach in Markov Decision Processes—0
A Continual Development Methodology for Large-scale Multitask Dynamic ML SystemsCode0
Learning Deep Optimal Embeddings with Sinkhorn Divergences—0
Vec2Face-v2: Unveil Human Faces from their Blackbox Features via Attention-based Network in Face Recognition—0
ScaleFace: Uncertainty-aware Deep Metric LearningCode0
Class-Specific Channel Attention for Few-Shot Learning—0
Latent Similarity Identifies Important Functional Connections for Phenotype PredictionCode0
Affective Manifolds: Modeling Machine's Mind to Like, Dislike, Enjoy, Suffer, Worry, Fear, and Feel Like A HumanCode0
Few-Shot Learning Meets Transformer: Unified Query-Support Transformers for Few-Shot Classification—0
Satellite Image Search in AgoraEO—0
Bidirectional Feature Globalization for Few-shot Semantic Segmentation of 3D Point Cloud Scenes—0
Regressing Relative Fine-Grained Change for Sub-Groups in Unreliable Heterogeneous Data Through Deep Multi-Task Metric Learning—0
Pairwise Learning via Stagewise Training in Proximal Setting—0
Unsupervised Graph Spectral Feature Denoising for Crop Yield Prediction—0
Content-Based Landmark Retrieval Combining Global and Local Features using Siamese Neural NetworksCode0
Semantic Data Augmentation based Distance Metric Learning for Domain Generalization—0
Re-thinking and Re-labeling LIDC-IDRI for Robust Pulmonary Cancer Prediction—0
Active Learning of Ordinal Embeddings: A User Study on Football Data—0
ConceptBeam: Concept Driven Target Speech Extraction—0
Rethinking Few-Shot Class-Incremental Learning with Open-Set Hypothesis in Hyperbolic Geometry—0
Bayesian Evidential Learning for Few-Shot Classification—0
WideResNet with Joint Representation Learning and Data Augmentation for Cover Song Identification—0
Cross Vision-RF Gait Re-identification with Low-cost RGB-D Cameras and mmWave Radars—0
Adversarial Reweighting for Speaker Verification Fairness—0
Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification—0
Dam reservoir extraction from remote sensing imagery using tailored metric learning strategiesCode0
Supervised similarity learning for corporate bonds using Random Forest proximities—0
One for All: Simultaneous Metric and Preference Learning over Multiple UsersCode0
Dynamic Contrastive Distillation for Image-Text Retrieval—0
Dummy Prototypical Networks for Few-Shot Open-Set Keyword Spotting—0
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All datasetsCARS196Stanford Online ProductsCUB-200-2011In-ShopDyML-AnimalDyML-ProductDyML-Vehicle

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Unicom+ViT-L@336pxR@198.2—Unverified
2Hyp-DINO 8x8R@192.8—Unverified
3ResNet-50 + AVSLR@191.5—Unverified
4NEDR@191.5—Unverified
5ResNet-50 + Intra-Batch (ensemble of 5)R@191.5—Unverified
6EfficientDML-VPTSP-G/512R@191.2—Unverified
7CCL (ResNet-50)R@191.02—Unverified
8ResNet50 + LanguageR@190.2—Unverified
9ResNet-50 + MetrixR@189.6—Unverified
10ResNet50 + S2SDR@189.5—Unverified
#ModelMetricClaimedVerifiedStatus
1Unicom+ViT-L@336pxR@191.2—Unverified
2STIRR@188.3—Unverified
3Recall@k Surrogate Loss (ViT-B/16)R@188—Unverified
4ViT-TripletR@186.5—Unverified
5ROADMAP (DeiT-S)R@186—Unverified
6Hyp-ViTR@185.9—Unverified
7Hyp-DINOR@185.1—Unverified
8Recall@k Surrogate Loss (ViT-B/32)R@185.1—Unverified
9CCL (ResNet-50)R@183.1—Unverified
10ROADMAP (ResNet-50)R@183.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Unicom+ViT-L@336pxR@190.1—Unverified
2EfficientDML-VPTSP-G/512R@188.5—Unverified
3Hyp-ViTR@185.6—Unverified
4Hyp-DINOR@180.9—Unverified
5NEDR@174.9—Unverified
6CCL (ResNet-50)R@173.45—Unverified
7ResNet-50 + AVSLR@171.9—Unverified
8ResNet-50 + Intra-Batch ConnectionsR@171.8—Unverified
9ResNet50 + LanguageR@171.4—Unverified
10ResNet-50 + MetrixR@171.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Unicom+ViT-L@336pxR@196.7—Unverified
2STIRR@195—Unverified
3MGAR@194.3—Unverified
4Hyp-ViTR@192.5—Unverified
5Hyp-DINOR@192.4—Unverified
6CCL (ResNet-50)R@192.31—Unverified
7Gradient SurgeryR@192.21—Unverified
8ResNet-50 + MetrixR@192.2—Unverified
9EfficientDML-VPTSP-G/512R@192.1—Unverified
10ViT-TripletR@192.1—Unverified
#ModelMetricClaimedVerifiedStatus
1HAPPIERAverage-mAP43.8—Unverified
2CSLAverage-mAP31—Unverified
#ModelMetricClaimedVerifiedStatus
1HAPPIERAverage-mAP38—Unverified
2CSLAverage-mAP28.7—Unverified
#ModelMetricClaimedVerifiedStatus
1HAPPIERAverage-mAP37—Unverified
2CSLAverage-mAP12.1—Unverified