SOTAVerified

Contrastive Learning

Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: Schroff et al. 2015)

Papers

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Showing 2251–2300 of 6661 papers

TitleStatusHype
MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patientsCode0
Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic ImagesCode0
Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential RecommendationCode0
CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive LearningCode0
Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric VideosCode0
CPCL: Cross-Modal Prototypical Contrastive Learning for Weakly Supervised Text-based Person Re-IdentificationCode0
Adversarial Self-Supervised Learning for Out-of-Domain DetectionCode0
MeDSLIP: Medical Dual-Stream Language-Image Pre-training for Fine-grained AlignmentCode0
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-TrainingCode0
MEDFORM: A Foundation Model for Contrastive Learning of CT Imaging and Clinical Numeric Data in Multi-Cancer AnalysisCode0
Counterfactual Cross-modality Reasoning for Weakly Supervised Video Moment LocalizationCode0
Adversarial Modality Alignment Network for Cross-Modal Molecule RetrievalCode0
MCRL4OR: Multimodal Contrastive Representation Learning for Off-Road Environmental PerceptionCode0
Medical Question Summarization with Entity-driven Contrastive LearningCode0
ARIEL: Adversarial Graph Contrastive LearningCode0
MassNet: A Deep Learning Approach for Body Weight Extraction from A Single Pressure ImageCode0
Mask-informed Deep Contrastive Incomplete Multi-view ClusteringCode0
Masking Improves Contrastive Self-Supervised Learning for ConvNets, and Saliency Tells You WhereCode0
Medication Recommendation via Dual Molecular Modalities and Multi-Step EnhancementCode0
CCFC: Bridging Federated Clustering and Contrastive LearningCode0
Masked Student Dataset of ExpressionsCode0
CoRTEx: Contrastive Learning for Representing Terms via Explanations with Applications on Constructing Biomedical Knowledge GraphsCode0
Correlation between Alignment-Uniformity and Performance of Dense Contrastive RepresentationsCode0
Causal integration of chemical structures improves representations of microscopy images for morphological profilingCode0
Masked Collaborative Contrast for Weakly Supervised Semantic SegmentationCode0
COROLLA: An Efficient Multi-Modality Fusion Framework with Supervised Contrastive Learning for Glaucoma GradingCode0
MarsEclipse at SemEval-2023 Task 3: Multi-Lingual and Multi-Label Framing Detection with Contrastive LearningCode0
Are Existing Out-Of-Distribution Techniques Suitable for Network Intrusion Detection?Code0
Coreference Graph Guidance for Mind-Map GenerationCode0
Adversarial Momentum-Contrastive Pre-TrainingCode0
Mask-Guided Contrastive Attention Model for Person Re-IdentificationCode0
MSA-UNet3+: Multi-Scale Attention UNet3+ with New Supervised Prototypical Contrastive Loss for Coronary DSA Image SegmentationCode0
Copy-Pasting Coherent Depth Regions Improves Contrastive Learning for Urban-Scene SegmentationCode0
Manifold Contrastive Learning with Variational Lie Group OperatorsCode0
COOKIE: Contrastive Cross-Modal Knowledge Sharing Pre-Training for Vision-Language RepresentationCode0
ManiNeg: Manifestation-guided Multimodal Pretraining for Mammography ClassificationCode0
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive LearningCode0
Making the Most of Text Semantics to Improve Biomedical Vision--Language ProcessingCode0
CATALOG: A Camera Trap Language-guided Contrastive Learning ModelCode0
Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt TuningCode0
A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing ModelsCode0
Conventional Contrastive Learning Often Falls Short: Improving Dense Retrieval with Cross-Encoder Listwise Distillation and Synthetic DataCode0
Machine Unlearning in Hyperbolic vs. Euclidean Multimodal Contrastive Learning: Adapting Alignment Calibration to MERUCode0
CASC-AI: Consensus-aware Self-corrective AI Agents for Noise Cell SegmentationCode0
Controlled Text Generation with Hidden Representation TransformationsCode0
MA-AVT: Modality Alignment for Parameter-Efficient Audio-Visual TransformersCode0
M3ANet: Multi-scale and Multi-Modal Alignment Network for Brain-Assisted Target Speaker ExtractionCode0
Making Pre-trained Language Models Better Continual Few-Shot Relation ExtractorsCode0
Mao-Zedong At SemEval-2023 Task 4: Label Represention Multi-Head Attention Model With Contrastive Learning-Enhanced Nearest Neighbor Mechanism For Multi-Label Text ClassificationCode0
Multi-task Pre-training Language Model for Semantic Network CompletionCode0
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All datasetsimagenet-1k10,000 People - Human Pose Recognition DataCIFAR-10STL-10

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6—Unverified
2ResNet50ImageNet Top-1 Accuracy73—Unverified
3ResNet50ImageNet Top-1 Accuracy71.1—Unverified
4ResNet50ImageNet Top-1 Accuracy69.3—Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6—Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8—Unverified
7ResNet50ImageNet Top-1 Accuracy63.6—Unverified
8ResNet50ImageNet Top-1 Accuracy61.5—Unverified
9ResNet50ImageNet Top-1 Accuracy61.5—Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3—Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55—Unverified