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

Showing 401425 of 6661 papers

TitleStatusHype
Contrastive Learning of Generalized Game RepresentationsCode1
A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-IdentificationCode1
A Contrastive Cross-Channel Data Augmentation Framework for Aspect-based Sentiment AnalysisCode1
A Self-supervised Method for Entity AlignmentCode1
CCL: Continual Contrastive Learning for LiDAR Place RecognitionCode1
Contrastive Representation DistillationCode1
Certifiably Robust Graph Contrastive LearningCode1
CETN: Contrast-enhanced Through Network for CTR PredictionCode1
Contrastive learning of global and local features for medical image segmentation with limited annotationsCode1
A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence EmbeddingsCode1
Bootstrapping Interactive Image-Text Alignment for Remote Sensing Image CaptioningCode1
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation OverlapCode1
A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect DetectionCode1
ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense RetrievalCode1
CL4CTR: A Contrastive Learning Framework for CTR PredictionCode1
A Simple Contrastive Learning Objective for Alleviating Neural Text DegenerationCode1
A simple, efficient and scalable contrastive masked autoencoder for learning visual representationsCode1
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised LearningCode1
CLAD: Robust Audio Deepfake Detection Against Manipulation Attacks with Contrastive LearningCode1
Bootstrapping meaning through listening: Unsupervised learning of spoken sentence embeddingsCode1
A Simple Graph Contrastive Learning Framework for Short Text ClassificationCode1
A Simple Long-Tailed Recognition Baseline via Vision-Language ModelCode1
CLCC: Contrastive Learning for Color ConstancyCode1
A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation ExtractionCode1
Anomaly Detection in IR Images of PV Modules using Supervised Contrastive LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6Unverified
2ResNet50ImageNet Top-1 Accuracy73Unverified
3ResNet50ImageNet Top-1 Accuracy71.1Unverified
4ResNet50ImageNet Top-1 Accuracy69.3Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8Unverified
7ResNet50ImageNet Top-1 Accuracy63.6Unverified
8ResNet50ImageNet Top-1 Accuracy61.5Unverified
9ResNet50ImageNet Top-1 Accuracy61.5Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55Unverified