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 4476–4500 of 6661 papers

TitleStatusHype
Multi-Similarity Contrastive Learning—0
Contrast Is All You Need—0
Fisher-Weighted Merge of Contrastive Learning Models in Sequential Recommendation—0
STS-CCL: Spatial-Temporal Synchronous Contextual Contrastive Learning for Urban Traffic Forecasting—0
Graph Contrastive Topic ModelCode0
SCAT: Robust Self-supervised Contrastive Learning via Adversarial Training for Text Classification—0
Relation-aware graph structure embedding with co-contrastive learning for drug-drug interaction prediction—0
Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biases—0
Learning Multi-Agent Communication with Contrastive Learning—0
Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesis—0
ENGAGE: Explanation Guided Data Augmentation for Graph Representation LearningCode0
Multiscale Progressive Text Prompt Network for Medical Image Segmentation—0
Multi-network Contrastive Learning Based on Global and Local Representations—0
Hybrid Distillation: Connecting Masked Autoencoders with Contrastive Learners—0
Semantic Positive Pairs for Enhancing Visual Representation Learning of Instance Discrimination methods—0
ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis—0
FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium SegmentationCode0
Generalized Out-of-distribution Fault Diagnosis (GOOFD) via Internal Contrastive Learning—0
Contrastive Multi-view Framework for Customer Lifetime Value Prediction—0
Hard Sample Mining Enabled Supervised Contrastive Feature Learning for Wind Turbine Pitch System Fault Diagnosis—0
Histopathology Image Classification using Deep Manifold Contrastive Learning—0
A Self-supervised Contrastive Learning Method for Grasp Outcomes Prediction—0
Real-time Seismic Intensity Prediction using Self-supervised Contrastive GNN for Earthquake Early Warning—0
Improving Reference-based Distinctive Image Captioning with Contrastive Rewards—0
Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation—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