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 4626–4650 of 6661 papers

TitleStatusHype
DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing—0
DSS: Synthesizing long Digital Ink using Data augmentation, Style encoding and Split generation—0
Dual Adversarial Perturbators Generate rich Views for Recommendation—0
Dual-Channel Latent Factor Analysis Enhanced Graph Contrastive Learning for Recommendation—0
Dual Circle Contrastive Learning-Based Blind Image Super-Resolution—0
Dual Contrastive Learning for General Face Forgery Detection—0
Dual Contrastive Learning for Spatio-temporal Representation—0
Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation—0
Dual-Domain CLIP-Assisted Residual Optimization Perception Model for Metal Artifact Reduction—0
Dual-domain Collaborative Denoising for Social Recommendation—0
Dual-Granularity Contrastive Learning for Session-based Recommendation—0
Dual-Scale Interest Extraction Framework with Self-Supervision for Sequential Recommendation—0
Dual Space Graph Contrastive Learning—0
Technical Approach for the EMI Challenge in the 8th Affective Behavior Analysis in-the-Wild Competition—0
DualToken: Towards Unifying Visual Understanding and Generation with Dual Visual Vocabularies—0
DU-Net based Unsupervised Contrastive Learning for Cancer Segmentation in Histology Images—0
DyCON: Dynamic Uncertainty-aware Consistency and Contrastive Learning for Semi-supervised Medical Image Segmentation—0
DyExplainer: Explainable Dynamic Graph Neural Networks—0
Dynamic Contrastive Distillation for Image-Text Retrieval—0
Dynamic Contrastive Skill Learning with State-Transition Based Skill Clustering and Dynamic Length Adjustment—0
Dynamic Graph Representation Learning for Depression Screening with Transformer—0
DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability—0
Dynamic Modality-Camera Invariant Clustering for Unsupervised Visible-Infrared Person Re-identification—0
Dynamic Modeling of Hand-Object Interactions via Tactile Sensing—0
Dynamic Modeling of Hand-Object Interactions via Tactile Sensing.—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