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 4226–4250 of 6661 papers

TitleStatusHype
Towards Novel Class Discovery: A Study in Novel Skin Lesions Clustering—0
Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics—0
CtxMIM: Context-Enhanced Masked Image Modeling for Remote Sensing Image Understanding—0
3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information—0
A dual-branch model with inter- and intra-branch contrastive loss for long-tailed recognition—0
CLIP-Hand3D: Exploiting 3D Hand Pose Estimation via Context-Aware Prompting—0
Inherit with Distillation and Evolve with Contrast: Exploring Class Incremental Semantic Segmentation Without Exemplar Memory—0
Transferability of Representations Learned using Supervised Contrastive Learning Trained on a Multi-Domain Dataset—0
Exploring Self-Supervised Contrastive Learning of Spatial Sound Event Representation—0
Investigating Deep Neural Network Architecture and Feature Extraction Designs for Sensor-based Human Activity Recognition—0
Contrastive Continual Multi-view Clustering with Filtered Structural Fusion—0
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels—0
M^33D: Learning 3D priors using Multi-Modal Masked Autoencoders for 2D image and video understanding—0
Robust Stance Detection: Understanding Public Perceptions in Social Media—0
Provable Training for Graph Contrastive LearningCode0
PARTICLE: Part Discovery and Contrastive Learning for Fine-grained RecognitionCode0
Boundary-Aware Proposal Generation Method for Temporal Action Localization—0
Speed Co-Augmentation for Unsupervised Audio-Visual Pre-training—0
Calibration-based Dual Prototypical Contrastive Learning Approach for Domain Generalization Semantic SegmentationCode0
HyperTrack: Neural Combinatorics for High Energy PhysicsCode0
Contrastive Speaker Embedding With Sequential Disentanglement—0
On the Sweet Spot of Contrastive Views for Knowledge-enhanced Recommendation—0
Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy—0
USL-Net: Uncertainty Self-Learning Network for Unsupervised Skin Lesion Segmentation—0
Masking Improves Contrastive Self-Supervised Learning for ConvNets, and Saliency Tells You WhereCode0
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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