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 4026–4050 of 6661 papers

TitleStatusHype
Classification and Clustering of Sentence-Level Embeddings of Scientific Articles Generated by Contrastive Learning—0
Classification of Mild Cognitive Impairment Based on Dynamic Functional Connectivity Using Spatio-Temporal Transformer—0
Classification of Seeds using Domain Randomization on Self-Supervised Learning Frameworks—0
Class Instance Balanced Learning for Long-Tailed Classification—0
Class Prototypes Based Contrastive Learning for Classifying Multi-Label and Fine-Grained Educational Videos—0
Class Relationship Embedded Learning for Source-Free Unsupervised Domain Adaptation—0
CLAWS: Contrastive Learning with hard Attention and Weak Supervision—0
Contrastive Representation Disentanglement for Clustering—0
CLCE: An Approach to Refining Cross-Entropy and Contrastive Learning for Optimized Learning Fusion—0
CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data—0
CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation—0
CLDA-YOLO: Visual Contrastive Learning Based Domain Adaptive YOLO Detector—0
CLDTA: Contrastive Learning based on Diagonal Transformer Autoencoder for Cross-Dataset EEG Emotion Recognition—0
CLEAR: Contrastive Learning for Sentence Representation—0
CLeaRForecast: Contrastive Learning of High-Purity Representations for Time Series Forecasting—0
ClearVision: Leveraging CycleGAN and SigLIP-2 for Robust All-Weather Classification in Traffic Camera Imagery—0
CLEP-GAN: An Innovative Approach to Subject-Independent ECG Reconstruction from PPG Signals—0
CLERF: Contrastive LEaRning for Full Range Head Pose Estimation—0
CLExtract: Recovering Highly Corrupted DVB/GSE Satellite Stream with Contrastive Learning—0
CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection—0
CLGNN: A Contrastive Learning-based GNN Model for Betweenness Centrality Prediction on Temporal Graphs—0
Click-through Rate Prediction with Auto-Quantized Contrastive Learning—0
CLICv2: Image Complexity Representation via Content Invariance Contrastive Learning—0
Generalizable Denoising of Microscopy Images using Generative Adversarial Networks and Contrastive Learning—0
Clinical Contrastive Learning for Biomarker Detection—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