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 2951–3000 of 6661 papers

TitleStatusHype
Specializing Small Language Models towards Complex Style Transfer via Latent Attribute Pre-TrainingCode0
Self-supervised Multi-view Clustering in Computer Vision: A Survey—0
Towards Better Modeling with Missing Data: A Contrastive Learning-based Visual Analytics Perspective—0
Traffic Scene Similarity: a Graph-based Contrastive Learning Approach—0
Object2Scene: Putting Objects in Context for Open-Vocabulary 3D Detection—0
Contrastive Learning for Enhancing Robust Scene Transfer in Vision-based Agile Flight—0
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input RepresentationCode1
DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery CluesCode1
MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision TransformerCode1
GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels—0
Leveraging Multi-lingual Positive Instances in Contrastive Learning to Improve Sentence Embedding—0
DA-RAW: Domain Adaptive Object Detection for Real-World Adverse Weather Conditions—0
Supervised Stochastic Neighbor Embedding Using Contrastive LearningCode0
A Generative Framework for Self-Supervised Facial Representation LearningCode0
CvFormer: Cross-view transFormers with Pre-training for fMRI Analysis of Human Brain—0
CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders—0
Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax EntropyCode0
Road Disease Detection based on Latent Domain Background Feature Separation and Suppression—0
DebCSE: Rethinking Unsupervised Contrastive Sentence Embedding Learning in the Debiasing Perspective—0
Hodge-Aware Contrastive Learning—0
Domain-Aware Augmentations for Unsupervised Online General Continual Learning—0
Instance Adaptive Prototypical Contrastive Embedding for Generalized Zero Shot Learning—0
Contrast-Phys+: Unsupervised and Weakly-supervised Video-based Remote Physiological Measurement via Spatiotemporal ContrastCode1
Multi-behavior Recommendation with SVD Graph Neural Networks—0
Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive LearningCode1
Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language ModelCode0
Learning from History: Task-agnostic Model Contrastive Learning for Image RestorationCode1
Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive LearningCode1
Grounded Language Acquisition From Object and Action Imagery—0
SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition—0
Enhancing Hyperedge Prediction with Context-Aware Self-Supervised LearningCode0
Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic Signals—0
OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion DataCode1
Panoptic Vision-Language Feature FieldsCode1
Multi3DRefer: Grounding Text Description to Multiple 3D ObjectsCode1
Graph-Aware Contrasting for Multivariate Time-Series ClassificationCode1
ImitationNet: Unsupervised Human-to-Robot Motion Retargeting via Shared Latent Space—0
DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data AugmentationCode1
Unified Contrastive Fusion Transformer for Multimodal Human Action Recognition—0
Latent Spatiotemporal Adaptation for Generalized Face Forgery Video Detection—0
Unsupervised Gaze-aware Contrastive Learning with Subject-specific Condition—0
Mask2Anomaly: Mask Transformer for Universal Open-set Segmentation—0
Prompt-based Context- and Domain-aware Pretraining for Vision and Language Navigation—0
Label-efficient Contrastive Learning-based model for nuclei detection and classification in 3D Cardiovascular Immunofluorescent Images—0
M(otion)-mode Based Prediction of Ejection Fraction using EchocardiogramsCode0
Toward High Quality Facial Representation LearningCode1
ConDA: Contrastive Domain Adaptation for AI-generated Text DetectionCode1
Progressive Attention Guidance for Whole Slide Vulvovaginal Candidiasis ScreeningCode0
Spatio-Temporal Contrastive Self-Supervised Learning for POI-level Crowd Flow Inference—0
Contrastive Learning as Kernel Approximation—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