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 6576–6600 of 6661 papers

TitleStatusHype
Subgraph Networks Based Contrastive Learning—0
Subgraph Retrieval Enhanced by Graph-Text Alignment for Commonsense Question Answering—0
Subject Representation Learning from EEG using Graph Convolutional Variational Autoencoders—0
Subset-Contrastive Multi-Omics Network Embedding—0
Revealing the Relationship Between Publication Bias and Chemical Reactivity with Contrastive Learning—0
Subtask-Aware Visual Reward Learning from Segmented Demonstrations—0
SudokuSens: Enhancing Deep Learning Robustness for IoT Sensing Applications using a Generative Approach—0
SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training—0
SuperCon: Supervised Contrastive Learning for Imbalanced Skin Lesion Classification—0
Super Encoding Network: Recursive Association of Multi-Modal Encoders for Video Understanding—0
Supervised Contrastive Block Disentanglement—0
Supervised Contrastive Learning and Feature Fusion for Improved Kinship Verification—0
Supervised Contrastive Learning Approach for Contextual Ranking—0
Supervised Contrastive Learning as Multi-Objective Optimization for Fine-Tuning Large Pre-trained Language Models—0
Supervised Contrastive Learning for Recommendation—0
Supervised Contrastive Learning for Accented Speech Recognition—0
Supervised contrastive learning for cell stage classification of animal embryos—0
Supervised Contrastive Learning for Cross-lingual Transfer Learning—0
Supervised Contrastive Learning for Fine-grained Chromosome Recognition—0
Supervised Contrastive Learning for Ordinal Engagement Measurement—0
Supervised Contrastive Learning for Snapshot Spectral Imaging Face Anti-Spoofing—0
Supervised contrastive learning from weakly-labeled audio segments for musical version matching—0
Supervised Contrastive Learning on Blended Images for Long-tailed Recognition—0
Supervised Contrastive Learning to Classify Paranasal Anomalies in the Maxillary Sinus—0
Supervised Contrastive Learning with Structure Inference for Graph Classification—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