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
ABO: Dataset and Benchmarks for Real-World 3D Object UnderstandingCode1
Deep learning-based person re-identification methods: A survey and outlook of recent works—0
DMML-Net: Deep Metametric Learning for Few-Shot Geographic Object Segmentation in Remote Sensing Imagery—0
Multi Proxy Anchor Family Loss for Several Types of Gradients—0
Reliable Probability Intervals For Classification Using Inductive Venn Predictors Based on Distance Learning—0
Incremental Class Learning using Variational Autoencoders with Similarity Learning—0
Robust and Decomposable Average Precision for Image RetrievalCode1
LDDMM-Face: Large Deformation Diffeomorphic Metric Learning for Cross-annotation Face AlignmentCode0
Two Regimes of Generalization for Non-Linear Metric Learning—0
Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence ClassificationCode1
From Graph Local Embedding to Deep Metric Learning—0
A Biology-Informed Similarity Metric for Simulated Patches of Human Cell Membrane—0
Assessing two novel distance-based loss functions for few-shot image classification—0
ConVAEr: Convolutional Variational AutoEncodeRs for incremental similarity learning—0
ASAP DML: Deep Metric Learning with Alternating Sets of Alternating Proxies—0
Understanding Metric Learning on Unit Hypersphere and Generating Better Examples for Adversarial Training—0
Metric Learning on Temporal Graphs via Few-Shot Examples—0
Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id—0
Quantifying point cloud realism through adversarially learned latent representations—0
GradML: A Gradient-based Loss for Deep Metric Learning—0
Memory Regulation and Alignment toward Generalizer RGB-Infrared PersonCode1
Self-Supervised Metric Learning With Graph Clustering For Speaker DiarizationCode0
Negative Sample Matters: A Renaissance of Metric Learning for Temporal GroundingCode1
Improving Deep Metric Learning by Divide and ConquerCode1
Few-shot Learning in Emotion Recognition of Spontaneous Speech Using a Siamese Neural Network with Adaptive Sample Pair Formation—0
Deep Metric Learning for Ground Images—0
SetMargin Loss applied to Deep Keystroke Biometrics with Circle Packing Interpretation—0
Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image ClassificationCode0
Recall@k Surrogate Loss with Large Batches and Similarity MixupCode1
Deep Relational Metric LearningCode1
Metric Learning in Multilingual Sentence Similarity Measurement for Document Alignment—0
LoOp: Looking for Optimal Hard Negative Embeddings for Deep Metric LearningCode0
Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth EstimationCode1
Task-Sensitive Concept Drift Detector with Constraint EmbeddingCode0
Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI—0
Continual Semi-Supervised Learning through Contrastive Interpolation ConsistencyCode0
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric LearningCode1
Towards Interpretable Deep Metric Learning with Structural MatchingCode1
Text Anchor Based Metric Learning for Small-footprint Keyword Spotting—0
Deep Metric Learning for Open World Semantic SegmentationCode1
WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud RegistrationCode1
Noise-Resistant Deep Metric Learning with Probabilistic Instance Filtering—0
LDDMM-Face: Large Deformation Diffeomorphic Metric Learning for Flexible and Consistent Face Alignment—0
A Unified Representation Learning Strategy for Open Relation Extraction with Ranked List Loss—0
Meta-learning for Classifying Previously Unseen Data Source into Previously Unseen Emotional CategoriesCode0
Bridging Gap between Image Pixels and Semantics via Supervision: A Survey—0
Multi-Scale Feature and Metric Learning for Relation Extraction—0
DISP6D: Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose EstimationCode1
Hierarchical Latent Relation Modeling for Collaborative Metric LearningCode0
Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time ModelsCode0
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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
3NEDR@191.5—Unverified
4ResNet-50 + Intra-Batch (ensemble of 5)R@191.5—Unverified
5ResNet-50 + AVSLR@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