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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 4326–4350 of 6661 papers

TitleStatusHype
Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance—0
Synergizing Contrastive Learning and Optimal Transport for 3D Point Cloud Domain Adaptation—0
PECon: Contrastive Pretraining to Enhance Feature Alignment between CT and EHR Data for Improved Pulmonary Embolism DiagnosisCode0
Forensic Histopathological Recognition via a Context-Aware MIL Network Powered by Self-Supervised Contrastive LearningCode0
Self-Supervised Representation Learning with Cross-Context Learning between Global and Hypercolumn Features—0
A Co-training Approach for Noisy Time Series Learning—0
Cross-Video Contextual Knowledge Exploration and Exploitation for Ambiguity Reduction in Weakly Supervised Temporal Action Localization—0
Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records—0
A Small and Fast BERT for Chinese Medical Punctuation RestorationCode0
FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious disease—0
Age Prediction From Face Images Via Contrastive Learning—0
Towards Discriminative Representations with Contrastive Instances for Real-Time UAV Tracking—0
Masked Momentum Contrastive Learning for Zero-shot Semantic Understanding—0
Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors—0
SupEuclid: Extremely Simple, High Quality OoD Detection with Supervised Contrastive Learning and Euclidean Distance—0
Information Theory-Guided Heuristic Progressive Multi-View Coding—0
Contrastive Graph Prompt-tuning for Cross-domain Recommendation—0
Contrastive Learning based Deep Latent Masking for Music Source Separation—0
Unilaterally Aggregated Contrastive Learning with Hierarchical Augmentation for Anomaly Detection—0
Quantile-based Maximum Likelihood Training for Outlier DetectionCode0
Prototypical Cross-domain Knowledge Transfer for Cervical Dysplasia Visual Inspection—0
Robust Fraud Detection via Supervised Contrastive Learning—0
HICL: Hashtag-Driven In-Context Learning for Social Media Natural Language UnderstandingCode0
Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation—0
Contrastive Learning for Non-Local Graphs with Multi-Resolution Structural Views—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