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 2501–2525 of 6661 papers

TitleStatusHype
Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning—0
Heterogeneous Temporal Hypergraph Neural Network—0
Heuristic Vision Pre-Training with Self-Supervised and Supervised Multi-Task Learning—0
Center-wise Local Image Mixture For Contrastive Representation Learning—0
CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning—0
Center Contrastive Loss for Metric Learning—0
HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation—0
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization—0
CellCLIP -- Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning—0
A comprehensive solution to retrieval-based chatbot construction—0
Learning Speech Representation From Contrastive Token-Acoustic Pretraining—0
ArSarcasm Shared Task: An Ensemble BERT Model for SarcasmDetection in Arabic Tweets—0
Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems—0
CEIA: CLIP-Based Event-Image Alignment for Open-World Event-Based Understanding—0
Just Functioning as a Hook for Two-Stage Referring Multi-Object Tracking—0
Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning—0
Heterogeneous Subgraph Network with Prompt Learning for Interpretable Depression Detection on Social Media—0
Refining Latent Representations: A Generative SSL Approach for Heterogeneous Graph Learning—0
CoViews: Adaptive Augmentation Using Cooperative Views for Enhanced Contrastive Learning—0
Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph—0
CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering—0
Counting Objects in a Robotic Hand—0
CATE Estimation With Potential Outcome Imputation From Local Regression—0
C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation—0
A Robust Contrastive Alignment Method For Multi-Domain Text Classification—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