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 2676–2700 of 6661 papers

TitleStatusHype
Image Classification Using a Diffusion Model as a Pre-Training Model—0
Contrastive Mutual Information Maximization for Binary Neural Networks—0
Contrastive Multi-view Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks—0
Contrastive Multi-view Framework for Customer Lifetime Value Prediction—0
Bures Joint Distribution Alignment with Dynamic Margin for Unsupervised Domain Adaptation—0
CSI: Contrastive Data Stratification for Interaction Prediction and its Application to Compound-Protein Interaction Prediction—0
Building Vision-Language Models on Solid Foundations with Masked Distillation—0
Contrastive Multi-Task Dense Prediction—0
An online algorithm for contrastive Principal Component Analysis—0
Identifying Shared Decodable Concepts in the Human Brain Using Image-Language Foundation Models—0
Identity-Aware Semi-Supervised Learning for Comic Character Re-Identification—0
Contrastive Multi-Modal Representation Learning for Spark Plug Fault Diagnosis—0
Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks—0
Contrastive Multi-Level Graph Neural Networks for Session-based Recommendation—0
Building an Enhanced Autoregressive Document Retriever Leveraging Supervised Contrastive Learning—0
Adversarial Contrastive Learning by Permuting Cluster Assignments—0
Contrastive Multi-graph Learning with Neighbor Hierarchical Sifting for Semi-supervised Text Classification—0
3D Graph Contrastive Learning for Molecular Property Prediction—0
Buffer is All You Need: Defending Federated Learning against Backdoor Attacks under Non-iids via Buffering—0
Identification of morphological fingerprint in perinatal brains using quasi-conformal mapping and contrastive learning—0
Progressive Domain Adaptation with Contrastive Learning for Object Detection in the Satellite Imagery—0
Identity-Disentangled Adversarial Augmentation for Self-supervised Learning—0
Contrastive Mean-Shift Learning for Generalized Category Discovery—0
Anomaly Detection via Multi-Scale Contrasted Memory—0
Adversarial Contrastive Estimation—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