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 451–475 of 6661 papers

TitleStatusHype
Cross-Domain Sentiment Classification with Contrastive Learning and Mutual Information MaximizationCode1
Cross-Domain Sentiment Classification with In-Domain Contrastive LearningCode1
Contrastive Learning for Unpaired Image-to-Image TranslationCode1
Cross-modal Causal Relation Alignment for Video Question GroundingCode1
Cross-Modal Contrastive Learning of Representations for Navigation using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental ConditionsCode1
Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud RegistrationCode1
Cross-Patch Dense Contrastive Learning for Semi-Supervised Segmentation of Cellular Nuclei in Histopathologic ImagesCode1
Context-self contrastive pretraining for crop type semantic segmentationCode1
Cross-View Geolocalization and Disaster Mapping with Street-View and VHR Satellite Imagery: A Case Study of Hurricane IANCode1
Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-trainingCode1
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted InstancesCode1
CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal ConversionCode1
Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging DataCode1
CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image SegmentationCode1
AIRCHITECT v2: Learning the Hardware Accelerator Design Space through Unified RepresentationsCode1
BECLR: Batch Enhanced Contrastive Few-Shot LearningCode1
ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical ContrastCode1
Data Augmentation-free Unsupervised Learning for 3D Point Cloud UnderstandingCode1
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the LeastCode1
Behavior Contrastive Learning for Unsupervised Skill DiscoveryCode1
A class-weighted supervised contrastive learning long-tailed bearing fault diagnosis approach using quadratic neural networkCode1
ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with GeneticsCode1
A Language Model based Framework for New Concept Placement in OntologiesCode1
DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing ModalitiesCode1
Automated Spatio-Temporal Graph Contrastive LearningCode1
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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