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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5126–5150 of 6661 papers

TitleStatusHype
Hierarchical discriminative learning improves visual representations of biomedical microscopy—0
Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations—0
Hierarchically Decoupled Spatial-Temporal Contrast for Self-supervised Video Representation Learning—0
Hierarchical Multi-Positive Contrastive Learning for Patent Image Retrieval—0
Hierarchical Self-supervised Representation Learning for Movie Understanding—0
Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning—0
Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection—0
Hierarchical Text Classification Using Contrastive Learning Informed Path Guided Hierarchy—0
High-dimensional learning of narrow neural networks—0
Higher-order Cross-structural Embedding Model for Time Series Analysis—0
High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text-attributed Graphs—0
Fusion Self-supervised Learning for Recommendation—0
HiLight: A Hierarchy-aware Light Global Model with Hierarchical Local ConTrastive Learning—0
HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights—0
Histopathology Image Classification using Deep Manifold Contrastive Learning—0
HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction—0
HNCSE: Advancing Sentence Embeddings via Hybrid Contrastive Learning with Hard Negatives—0
Hodge-Aware Contrastive Learning—0
Homography augumented momentum constrastive learning for SAR image retrieval—0
Homophily-aware Heterogeneous Graph Contrastive Learning—0
Homophily-Driven Sanitation View for Robust Graph Contrastive Learning—0
HoughCL: Finding Better Positive Pairs in Dense Self-supervised Learning—0
How do Cross-View and Cross-Modal Alignment Affect Representations in Contrastive Learning?—0
How does Contrastive Pre-training Connect Disparate Domains?—0
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?—0
Show:102550
← PrevPage 206 of 267Next →
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