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 5351–5375 of 6661 papers

TitleStatusHype
Multimodal contrastive learning for remote sensing tasks—0
Language-aware Domain Generalization Network for Cross-Scene Hyperspectral Image Classification—0
Progressive Domain Adaptation with Contrastive Learning for Object Detection in the Satellite Imagery—0
SimCLF: A Simple Contrastive Learning Framework for Function-level Binary EmbeddingsCode0
Supervised Contrastive Learning to Classify Paranasal Anomalies in the Maxillary Sinus—0
Design of the topology for contrastive visual-textual alignmentCode0
Disentangled Graph Contrastive Learning for Review-based Recommendation—0
Joint Prediction of Meningioma Grade and Brain Invasion via Task-Aware Contrastive LearningCode0
Representative Image Feature Extraction via Contrastive Learning Pretraining for Chest X-ray Report Generation—0
Single-source Domain Expansion Network for Cross-Scene Hyperspectral Image Classification—0
Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing LabelsCode0
Semi-Supervised Semantic Segmentation with Cross Teacher TrainingCode0
EEG-based Emotion Recognition via Efficient Convolutional Neural Network and Contrastive Learning—0
IMG2IMU: Translating Knowledge from Large-Scale Images to IMU Sensing Applications—0
A Novel Approach for Pill-Prescription Matching with GNN Assistance and Contrastive LearningCode0
Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic ImagesCode0
Temporal Contrastive Learning with Curriculum—0
ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image ClassificationCode0
Focus-Driven Contrastive Learniang for Medical Question SummarizationCode0
Distilling Multi-Scale Knowledge for Event Temporal Relation Extraction—0
Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax—0
Incorporating Task-specific Concept Knowledge into Script LearningCode0
Let Me Check the Examples: Enhancing Demonstration Learning via Explicit Imitation—0
Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised LearningCode0
Modeling Adaptive Fine-grained Task Relatedness for Joint CTR-CVR Estimation—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