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 5576–5600 of 6661 papers

TitleStatusHype
Enhancing Sequential Recommendation with Graph Contrastive Learning—0
Analysis of Augmentations for Contrastive ECG Representation Learning—0
Micro-Expression Recognition Based on Attribute Information Embedding and Cross-modal Contrastive Learning—0
FaIRCoP: Facial Image Retrieval using Contrastive Personalization—0
Bayesian Robust Graph Contrastive LearningCode0
Image Harmonization with Region-wise Contrastive Learning—0
Unsupervised learning of features and object boundaries from local prediction—0
Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation—0
Triangular Contrastive Learning on Molecular Graphs—0
Fine-grained Contrastive Learning for Relation ExtractionCode0
Interaction of a priori Anatomic Knowledge with Self-Supervised Contrastive Learning in Cardiac Magnetic Resonance Imaging—0
Region-aware Knowledge Distillation for Efficient Image-to-Image Translation—0
SCVRL: Shuffled Contrastive Video Representation Learning—0
RecipeRec: A Heterogeneous Graph Learning Model for Recipe RecommendationCode0
An Adaptive Contrastive Learning Model for Spike Sorting—0
ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification—0
Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities—0
Conditional Supervised Contrastive Learning for Fair Text ClassificationCode0
Multi-Temporal Spatial-Spectral Comparison Network for Hyperspectral Anomalous Change Detection—0
Contrastive Learning of Coarse-Grained Force Fields—0
Improving Long Tailed Document-Level Relation Extraction via Easy Relation Augmentation and Contrastive Learning—0
Robust Task-Oriented Dialogue Generation with Contrastive Pre-training and Adversarial Filtering—0
Data Augmentation for Compositional Data: Advancing Predictive Models of the MicrobiomeCode0
Label-invariant Augmentation for Semi-Supervised Graph Classification—0
Personalized Prompt for Sequential Recommendation—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