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 351–375 of 6661 papers

TitleStatusHype
Prompting Vision-Language Model for Nuclei Instance Segmentation and ClassificationCode0
Deep Learning for Forensic Identification of SourceCode0
TAR: Teacher-Aligned Representations via Contrastive Learning for Quadrupedal Locomotion—0
Contrastive Learning Guided Latent Diffusion Model for Image-to-Image Translation—0
DINeMo: Learning Neural Mesh Models with no 3D Annotations—0
Learnable Sequence Augmenter for Triplet Contrastive Learning in Sequential Recommendation—0
Large Language Models Meet Contrastive Learning: Zero-Shot Emotion Recognition Across LanguagesCode0
Taxonomy Inference for Tabular Data Using Large Language Models—0
SeLIP: Similarity Enhanced Contrastive Language Image Pretraining for Multi-modal Head MRI—0
Scaling Vision Pre-Training to 4K ResolutionCode7
A-MESS: Anchor based Multimodal Embedding with Semantic Synchronization for Multimodal Intent Recognition—0
SuperFlow++: Enhanced Spatiotemporal Consistency for Cross-Modal Data PretrainingCode2
Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image AnalysisCode2
On the Perception Bottleneck of VLMs for Chart UnderstandingCode0
Linguistics-aware Masked Image Modeling for Self-supervised Scene Text RecognitionCode1
OCCO: LVM-guided Infrared and Visible Image Fusion Framework based on Object-aware and Contextual COntrastive Learning—0
Instruct-CLIP: Improving Instruction-Guided Image Editing with Automated Data Refinement Using Contrastive LearningCode1
Unsupervised Detection of Fraudulent Transactions in E-commerce Using Contrastive Learning—0
RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated DataCode0
Anchor-based oversampling for imbalanced tabular data via contrastive and adversarial learning—0
Structuring Scientific Innovation: A Framework for Modeling and Discovering Impactful Knowledge Combinations—0
Recommendation System in Advertising and Streaming Media: Unsupervised Data Enhancement Sequence Suggestions—0
What Time Tells Us? An Explorative Study of Time Awareness Learned from Static Images—0
Does GCL Need a Large Number of Negative Samples? Enhancing Graph Contrastive Learning with Effective and Efficient Negative SamplingCode0
Enhancing Persona Consistency for LLMs' Role-Playing using Persona-Aware Contrastive Learning—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