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 4551–4600 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
Disentangled Graph Contrastive Learning for Review-based Recommendation—0
Disentangle Perceptual Learning through Online Contrastive Learning—0
Disentangling Learnable and Memorizable Data via Contrastive Learning for Semantic Communications—0
Dissecting Representation Misalignment in Contrastive Learning via Influence Function—0
Distance-rank Aware Sequential Reward Learning for Inverse Reinforcement Learning with Sub-optimal Demonstrations—0
Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification—0
Distillation with Contrast is All You Need for Self-Supervised Point Cloud Representation Learning—0
Distill CLIP (DCLIP): Enhancing Image-Text Retrieval via Cross-Modal Transformer Distillation—0
Distilling Localization for Self-Supervised Representation Learning—0
Distilling Structured Knowledge for Text-Based Relational Reasoning—0
Distortion-Disentangled Contrastive Learning—0
Distributed Contrastive Learning for Medical Image Segmentation—0
Decentralized Unsupervised Learning of Visual Representations—0
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images—0
Divide and Contrast: Self-supervised Learning from Uncurated Data—0
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning—0
DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action Recognition—0
DMT: Comprehensive Distillation with Multiple Self-supervised Teachers—0
DN-CL: Deep Symbolic Regression against Noise via Contrastive Learning—0
DOA-Aware Audio-Visual Self-Supervised Learning for Sound Event Localization and Detection—0
Do Audio-Language Models Understand Linguistic Variations?—0
DocVideoQA: Towards Comprehensive Understanding of Document-Centric Videos through Question Answering—0
DOGR: Leveraging Document-Oriented Contrastive Learning in Generative Retrieval—0
Domain Adaptation for Semantic Segmentation via Patch-Wise Contrastive Learning—0
Domain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization—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