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 3051–3075 of 6661 papers

TitleStatusHype
Masked Momentum Contrastive Learning for Zero-shot Semantic Understanding—0
Decoupled Contrastive Multi-View Clustering with High-Order Random WalksCode1
Towards Discriminative Representations with Contrastive Instances for Real-Time UAV Tracking—0
MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for RecommendationCode1
Composed Image Retrieval using Contrastive Learning and Task-oriented CLIP-based FeaturesCode1
SupEuclid: Extremely Simple, High Quality OoD Detection with Supervised Contrastive Learning and Euclidean Distance—0
Contrastive Graph Prompt-tuning for Cross-domain Recommendation—0
Information Theory-Guided Heuristic Progressive Multi-View Coding—0
Contrastive Learning based Deep Latent Masking for Music Source Separation—0
Quantile-based Maximum Likelihood Training for Outlier DetectionCode0
Unilaterally Aggregated Contrastive Learning with Hierarchical Augmentation for Anomaly Detection—0
Prototypical Cross-domain Knowledge Transfer for Cervical Dysplasia Visual Inspection—0
HICL: Hashtag-Driven In-Context Learning for Social Media Natural Language UnderstandingCode0
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method—0
Contrastive Learning-based Imputation-Prediction Networks for In-hospital Mortality Risk Modeling using EHRsCode0
Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation—0
Robust Fraud Detection via Supervised Contrastive Learning—0
3D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose EstimationCode1
Contrastive Learning for Non-Local Graphs with Multi-Resolution Structural Views—0
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly DetectionCode1
Rethinking Image Forgery Detection via Soft Contrastive Learning and Unsupervised ClusteringCode1
Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos—0
Meta-ZSDETR: Zero-shot DETR with Meta-learning—0
Decoupled conditional contrastive learning with variable metadata for prostate lesion detectionCode0
Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning—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