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 5576–5600 of 6661 papers

TitleStatusHype
Looking Similar Sounding Different: Leveraging Counterfactual Cross-Modal Pairs for Audiovisual Representation Learning—0
LoopSR: Looping Sim-and-Real for Lifelong Policy Adaptation of Legged Robots—0
Loss Function Entropy Regularization for Diverse Decision Boundaries—0
Low-Entropy Latent Variables Hurt Out-of-Distribution Performance—0
Low-Light Image Enhancement by Learning Contrastive Representations in Spatial and Frequency Domains—0
Low-Rank Graph Contrastive Learning for Node Classification—0
LRC-BERT: Latent-representation Contrastive Knowledge Distillation for Natural Language Understanding—0
Lung-CADex: Fully automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT Images—0
M2HGCL: Multi-Scale Meta-Path Integrated Heterogeneous Graph Contrastive Learning—0
M^33D: Learning 3D priors using Multi-Modal Masked Autoencoders for 2D image and video understanding—0
M3PT: A Multi-Modal Model for POI Tagging—0
M5Product: Self-harmonized Contrastive Learning for E-commercial Multi-modal Pretraining—0
MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion—0
Machine Learning Methods for Gene Regulatory Network Inference—0
Machine Unlearning in Contrastive Learning—0
MACK: Mismodeling Addressed with Contrastive Knowledge—0
Maintenance Required: Updating and Extending Bootstrapped Human Activity Recognition Systems for Smart Homes—0
Make Domain Shift a Catastrophic Forgetting Alleviator in Class-Incremental Learning—0
Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking—0
Manifold-aware Representation Learning for Degradation-agnostic Image Restoration—0
Manipulating the Label Space for In-Context Classification—0
Manta: Enhancing Mamba for Few-Shot Action Recognition of Long Sub-Sequence—0
Manual-PA: Learning 3D Part Assembly from Instruction Diagrams—0
Many or Few Samples? Comparing Transfer, Contrastive and Meta-Learning in Encrypted Traffic Classification—0
Marginal Contrastive Correspondence for Guided Image Generation—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