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 4551–4575 of 6661 papers

TitleStatusHype
Dictionary Learning by Dynamical Neural Networks—0
DiffAVA: Personalized Text-to-Audio Generation with Visual Alignment—0
Diff-CL: A Novel Cross Pseudo-Supervision Method for Semi-supervised Medical Image Segmentation—0
Difficulty-Based Sampling for Debiased Contrastive Representation Learning—0
Difficulty-Focused Contrastive Learning for Knowledge Tracing with a Large Language Model-Based Difficulty Prediction—0
DiffRetouch: Using Diffusion to Retouch on the Shoulder of Experts—0
DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model—0
Diffusion-augmented Graph Contrastive Learning for Collaborative Filter—0
Diffusion Models as Masked Audio-Video Learners—0
DiffusionTalker: Personalization and Acceleration for Speech-Driven 3D Face Diffuser—0
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data—0
DimCL: Dimensional Contrastive Learning For Improving Self-Supervised Learning—0
DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation—0
DINeMo: Learning Neural Mesh Models with no 3D Annotations—0
Directed Link Prediction using GNN with Local and Global Feature Fusion—0
Direction-Aware Hybrid Representation Learning for 3D Hand Pose and Shape Estimation—0
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences—0
DisCo: Graph-Based Disentangled Contrastive Learning for Cold-Start Cross-Domain Recommendation—0
Enforcing View-Consistency in Class-Agnostic 3D Segmentation Fields—0
Discovering COVID-19 Coughing and Breathing Patterns from Unlabeled Data Using Contrastive Learning with Varying Pre-Training Domains—0
Discrepant and Multi-Instance Proxies for Unsupervised Person Re-Identification—0
Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving—0
Discriminative Speaker Representation via Contrastive Learning with Class-Aware Attention in Angular Space—0
Disentangled Contrastive Image Translation for Nighttime Surveillance—0
Disentangled Contrastive Learning on Graphs—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