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 1976–2000 of 6661 papers

TitleStatusHype
Event-enhanced Retrieval in Real-time SearchCode0
ActNetFormer: Transformer-ResNet Hybrid Method for Semi-Supervised Action Recognition in VideosCode0
ClinLinker: Medical Entity Linking of Clinical Concept Mentions in Spanish—0
LLM2Vec: Large Language Models Are Secretly Powerful Text EncodersCode5
Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures—0
Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image Clustering—0
CDAD-Net: Bridging Domain Gaps in Generalized Category Discovery—0
Anatomical Conditioning for Contrastive Unpaired Image-to-Image Translation of Optical Coherence Tomography ImagesCode0
A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images—0
Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models—0
TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis—0
DWE+: Dual-Way Matching Enhanced Framework for Multimodal Entity LinkingCode0
DELTA: Decoupling Long-Tailed Online Continual LearningCode0
On Exploring PDE Modeling for Point Cloud Video Representation LearningCode0
PIE: Physics-inspired Low-light Enhancement—0
IITK at SemEval-2024 Task 1: Contrastive Learning and Autoencoders for Semantic Textual Relatedness in Multilingual TextsCode0
Adaptive Intra-Class Variation Contrastive Learning for Unsupervised Person Re-Identification—0
Image-Text Co-Decomposition for Text-Supervised Semantic SegmentationCode1
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences—0
Effective Lymph Nodes Detection in CT Scans Using Location Debiased Query Selection and Contrastive Query Representation in Transformer—0
Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach—0
Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive LearningCode1
Decoupling Static and Hierarchical Motion Perception for Referring Video SegmentationCode2
Multi Positive Contrastive Learning with Pose-Consistent Generated Images—0
A Comprehensive Survey on Self-Supervised Learning for RecommendationCode2
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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