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 1051–1075 of 6661 papers

TitleStatusHype
Pseudo-Label Enhanced Prototypical Contrastive Learning for Uniformed Intent DiscoveryCode0
Human-Object Interaction Detection Collaborated with Large Relation-driven Diffusion Models—0
CLOUDSPAM: Contrastive Learning On Unlabeled Data for Segmentation and Pre-Training Using Aggregated Point Clouds and MoCoCode0
CLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimationCode0
ShifCon: Enhancing Non-Dominant Language Capabilities with a Shift-based Contrastive FrameworkCode0
A contrastive-learning approach for auditory attention detection—0
Enhancing pretraining efficiency for medical image segmentation via transferability metricsCode0
Enhancing Multimodal Medical Image Classification using Cross-Graph Modal Contrastive LearningCode0
Rethinking Positive Pairs in Contrastive Learning—0
EntityCLIP: Entity-Centric Image-Text Matching via Multimodal Attentive Contrastive Learning—0
Time and Frequency Synergy for Source-Free Time-Series Domain Adaptations—0
FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive LearningCode0
SRA: A Novel Method to Improve Feature Embedding in Self-supervised Learning for Histopathological Images—0
Double Banking on Knowledge: Customized Modulation and Prototypes for Multi-Modality Semi-supervised Medical Image Segmentation—0
SigCLR: Sigmoid Contrastive Learning of Visual Representations—0
Prototype and Instance Contrastive Learning for Unsupervised Domain Adaptation in Speaker Verification—0
Bridging the Modality Gap: Dimension Information Alignment and Sparse Spatial Constraint for Image-Text Matching—0
Unsupervised Time Series Anomaly Prediction with Importance-based Generative Contrastive Learning—0
Progressive Compositionality In Text-to-Image Generative ModelsCode1
EPContrast: Effective Point-level Contrastive Learning for Large-scale Point Cloud Understanding—0
Contrastive random lead coding for channel-agnostic self-supervision of biosignals—0
Promoting cross-modal representations to improve multimodal foundation models for physiological signals—0
Do Audio-Language Models Understand Linguistic Variations?—0
Enhancing Multimodal Affective Analysis with Learned Live Comment Features—0
MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast—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