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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2301–2325 of 6661 papers

TitleStatusHype
A sound description: Exploring prompt templates and class descriptions to enhance zero-shot audio classification—0
CLAWS: Contrastive Learning with hard Attention and Weak Supervision—0
Debias-CLR: A Contrastive Learning Based Debiasing Method for Algorithmic Fairness in Healthcare Applications—0
DebCSE: Rethinking Unsupervised Contrastive Sentence Embedding Learning in the Debiasing Perspective—0
A Soft Contrastive Learning-based Prompt Model for Few-shot Sentiment Analysis—0
Liquidity takers behavior representation through a contrastive learning approach—0
Geometric Graph Representation Learning via Maximizing Rate Reduction—0
DD-rPPGNet: De-interfering and Descriptive Feature Learning for Unsupervised rPPG Estimation—0
Class Relationship Embedded Learning for Source-Free Unsupervised Domain Adaptation—0
A Contrastive Learning Based Convolutional Neural Network for ERP Brain-Computer Interfaces—0
Class Prototypes Based Contrastive Learning for Classifying Multi-Label and Fine-Grained Educational Videos—0
DCOR: Anomaly Detection in Attributed Networks via Dual Contrastive Learning Reconstruction—0
A Generic Self-Supervised Framework of Learning Invariant Discriminative Features—0
Learning Actionable World Models for Industrial Process Control—0
Class Instance Balanced Learning for Long-Tailed Classification—0
GeoCLR: Georeference Contrastive Learning for Efficient Seafloor Image Interpretation—0
Geometric Anchor Correspondence Mining With Uncertainty Modeling for Universal Domain Adaptation—0
Dcl-Net: Dual Contrastive Learning Network for Semi-Supervised Multi-Organ Segmentation—0
Classification of Seeds using Domain Randomization on Self-Supervised Learning Frameworks—0
A Simplifying and Learnable Graph Convolutional Attention Network for Unsupervised Knowledge Graphs Alignment—0
DB-GNN: Dual-Branch Graph Neural Network with Multi-Level Contrastive Learning for Jointly Identifying Within- and Cross-Frequency Coupled Brain Networks—0
Classification of Mild Cognitive Impairment Based on Dynamic Functional Connectivity Using Spatio-Temporal Transformer—0
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection—0
DAug: Diffusion-based Channel Augmentation for Radiology Image Retrieval and Classification—0
A Simplified Framework for Contrastive Learning for Node Representations—0
Show:102550
← PrevPage 93 of 267Next →
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