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 3176–3200 of 6661 papers

TitleStatusHype
Learning Dense Correspondences between Photos and Sketches—0
Homophily-Driven Sanitation View for Robust Graph Contrastive Learning—0
General-Purpose Multi-Modal OOD Detection Framework—0
PRIOR: Prototype Representation Joint Learning from Medical Images and ReportsCode1
SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image SegmentationCode1
Multi-View Vertebra Localization and Identification from CT ImagesCode1
Rule By Example: Harnessing Logical Rules for Explainable Hate Speech DetectionCode0
Hierarchical Skeleton Meta-Prototype Contrastive Learning with Hard Skeleton Mining for Unsupervised Person Re-IdentificationCode1
Towards a Visual-Language Foundation Model for Computational Pathology—0
Enhancing Human-like Multi-Modal Reasoning: A New Challenging Dataset and Comprehensive FrameworkCode1
ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT DenoisingCode1
Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG SignalCode0
Hallucination Improves the Performance of Unsupervised Visual Representation Learning—0
Extracting Molecular Properties from Natural Language with Multimodal Contrastive Learning—0
Edge Guided GANs with Multi-Scale Contrastive Learning for Semantic Image SynthesisCode1
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images—0
Learning Discriminative Visual-Text Representation for Polyp Re-IdentificationCode0
Language-Enhanced Session-Based Recommendation with Decoupled Contrastive Learning—0
Extreme Multi-Label Skill Extraction Training using Large Language Models—0
Identical and Fraternal Twins: Fine-Grained Semantic Contrastive Learning of Sentence Representations—0
Density-invariant Features for Distant Point Cloud RegistrationCode1
Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive LearningCode1
Semantic-Aware Dual Contrastive Learning for Multi-label Image ClassificationCode1
Multi-Grained Multimodal Interaction Network for Entity LinkingCode1
Space Engage: Collaborative Space Supervision for Contrastive-based Semi-Supervised Semantic Segmentation—0
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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