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 2751–2775 of 6661 papers

TitleStatusHype
Metric Learning for 3D Point Clouds Using Optimal Transport—0
FISHing in Uncertainty: Synthetic Contrastive Learning for Genetic Aberration DetectionCode0
Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis—0
Topology-Aware Graph Augmentation for Predicting Clinical Trajectories in Neurocognitive Disorders—0
Multi-modal Spatial Clustering for Spatial Transcriptomics Utilizing High-resolution Histology Images—0
BioNCERE: Non-Contrastive Enhancement For Relation Extraction In Biomedical Texts—0
JEMA: A Joint Embedding Framework for Scalable Co-Learning with Multimodal Alignment—0
Towards Cross-Modal Text-Molecule Retrieval with Better Modality AlignmentCode0
Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation—0
DOA-Aware Audio-Visual Self-Supervised Learning for Sound Event Localization and Detection—0
PARDON: Privacy-Aware and Robust Federated Domain GeneralizationCode0
Contrastive Learning and Adversarial Disentanglement for Task-Oriented Semantic CommunicationsCode0
Higher-order Cross-structural Embedding Model for Time Series Analysis—0
Dataset Awareness is not Enough: Implementing Sample-level Tail Encouragement in Long-tailed Self-supervised Learning—0
A Fresh Look at Generalized Category Discovery through Non-negative Matrix Factorization—0
Revisiting Multi-Granularity Representation via Group Contrastive Learning for Unsupervised Vehicle Re-identification—0
Unsupervised Multimodal Fusion of In-process Sensor Data for Advanced Manufacturing Process Monitoring—0
Emotion-Guided Image to Music Generation—0
PepDoRA: A Unified Peptide Language Model via Weight-Decomposed Low-Rank Adaptation—0
Breccia and basalt classification of thin sections of Apollo rocks with deep learningCode0
Relation-based Counterfactual Data Augmentation and Contrastive Learning for Robustifying Natural Language Inference Models—0
Fidelity-Imposed Displacement Editing for the Learn2Reg 2024 SHG-BF Challenge—0
Beyond Positive History: Re-ranking with List-level Hybrid Feedback—0
Enhancing CTR Prediction in Recommendation Domain with Search Query Representation—0
ANOMIX: A Simple yet Effective Hard Negative Generation via Mixing for Graph Anomaly DetectionCode0
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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