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 4251–4275 of 6661 papers

TitleStatusHype
A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation—0
DimCL: Dimensional Contrastive Learning For Improving Self-Supervised Learning—0
Multi-level Asymmetric Contrastive Learning for Volumetric Medical Image Segmentation Pre-trainingCode0
BELT:Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervision—0
SCOB: Universal Text Understanding via Character-wise Supervised Contrastive Learning with Online Text Rendering for Bridging Domain GapCode0
Self-Supervised Contrastive Learning for Robust Audio-Sheet Music Retrieval Systems—0
BitCoin: Bidirectional Tagging and Supervised Contrastive Learning based Joint Relational Triple Extraction Framework—0
Audio Contrastive based Fine-tuning—0
Visual Question Answering in the Medical Domain—0
Semi-supervised News Discourse Profiling with Contrastive Learning—0
Leveraging Negative Signals with Self-Attention for Sequential Music Recommendation—0
Specializing Small Language Models towards Complex Style Transfer via Latent Attribute Pre-TrainingCode0
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation—0
Motif-Centric Representation Learning for Symbolic MusicCode0
Self-supervised Multi-view Clustering in Computer Vision: A Survey—0
Object2Scene: Putting Objects in Context for Open-Vocabulary 3D Detection—0
Towards Better Modeling with Missing Data: A Contrastive Learning-based Visual Analytics Perspective—0
Contrastive Learning for Enhancing Robust Scene Transfer in Vision-based Agile Flight—0
Traffic Scene Similarity: a Graph-based Contrastive Learning Approach—0
Leveraging Multi-lingual Positive Instances in Contrastive Learning to Improve Sentence Embedding—0
GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels—0
A Generative Framework for Self-Supervised Facial Representation LearningCode0
DA-RAW: Domain Adaptive Object Detection for Real-World Adverse Weather Conditions—0
Supervised Stochastic Neighbor Embedding Using Contrastive LearningCode0
Road Disease Detection based on Latent Domain Background Feature Separation and Suppression—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