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 5826–5850 of 6661 papers

TitleStatusHype
Multi-Scale Spatio-Temporal Graph Convolutional Network for Facial Expression Spotting—0
Multi-Scale Subgraph Contrastive Learning—0
Multi-Scale Video Anomaly Detection by Multi-Grained Spatio-Temporal Representation Learning—0
Multi-Similarity Contrastive Learning—0
Multi-Source domain adaptation via supervised contrastive learning and confident consistency regularization—0
Boosting Multi-Speaker Expressive Speech Synthesis with Semi-supervised Contrastive Learning—0
Multi-Stage Coarse-to-Fine Contrastive Learning for Conversation Intent Induction—0
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching—0
Multi-stage Retrieve and Re-rank Model for Automatic Medical Coding Recommendation—0
Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings—0
Multi-Task Curriculum Graph Contrastive Learning with Clustering Entropy Guidance—0
Multi-Task Self-Supervised Time-Series Representation Learning—0
Shifting Transformation Learning for Out-of-Distribution Detection—0
Multi-Temporal Spatial-Spectral Comparison Network for Hyperspectral Anomalous Change Detection—0
Multi-View Adaptive Contrastive Learning for Information Retrieval Based Fault Localization—0
Self-supervised Remote Sensing Images Change Detection at Pixel-level—0
Multiview Contrastive Learning for Unsupervised Domain Adaptation in Brain–Computer Interfaces—0
Multi-view Contrastive Learning with Additive Margin for Adaptive Nasopharyngeal Carcinoma Radiotherapy Prediction—0
Multi-View Correlation Consistency for Semi-Supervised Semantic Segmentation—0
Multi-View Dreaming: Multi-View World Model with Contrastive Learning—0
Multi-view Fake News Detection Model Based on Dynamic Hypergraph—0
Multi-view Feature Extraction based on Dual Contrastive Head—0
Multi-view Feature Extraction based on Triple Contrastive Heads—0
Multi-view Granular-ball Contrastive Clustering—0
Multi-View Incongruity Learning for Multimodal Sarcasm Detection—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