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 2951–3000 of 6661 papers

TitleStatusHype
Learning Spatially-Aware Language and Audio Embeddings—0
Contrastive Learning for Character Detection in Ancient Greek PapyriCode0
MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion—0
Learning Semi-Supervised Medical Image Segmentation from Spatial RegistrationCode0
NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training—0
Pre-Training for 3D Hand Pose Estimation with Contrastive Learning on Large-Scale Hand Images in the Wild—0
Self-supervised Learning for Acoustic Few-Shot Classification—0
Open-World Test-Time Training: Self-Training with Contrast Learning—0
Towards Multi-view Graph Anomaly Detection with Similarity-Guided Contrastive Clustering—0
Turbo your multi-modal classification with contrastive learning—0
Acoustic identification of individual animals with hierarchical contrastive learning—0
Multi-intent Aware Contrastive Learning for Sequential Recommendation—0
Expediting and Elevating Large Language Model Reasoning via Hidden Chain-of-Thought Decoding—0
Multiplex Graph Contrastive Learning with Soft NegativesCode0
GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning—0
BLens: Contrastive Captioning of Binary Functions using Ensemble Embedding—0
Taylor-Sensus Network: Embracing Noise to Enlighten Uncertainty for Scientific Data—0
Multi-object event graph representation learning for Video Question Answering—0
A Unified Contrastive Loss for Self-TrainingCode0
VMAS: Video-to-Music Generation via Semantic Alignment in Web Music Videos—0
What to align in multimodal contrastive learning?—0
Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive LearningCode0
Alignist: CAD-Informed Orientation Distribution Estimation by Fusing Shape and CorrespondencesCode0
Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point CloudsCode0
INTRA: Interaction Relationship-aware Weakly Supervised Affordance Grounding—0
How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval—0
PharmacoMatch: Efficient 3D Pharmacophore Screening via Neural Subgraph Matching—0
LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs—0
Contrastive Federated Learning with Tabular Data Silos—0
DV-FSR: A Dual-View Target Attack Framework for Federated Sequential RecommendationCode0
CustomContrast: A Multilevel Contrastive Perspective For Subject-Driven Text-to-Image Customization—0
Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models—0
Enhancing Graph Contrastive Learning with Reliable and Informative Augmentation for RecommendationCode0
ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors—0
GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning—0
GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priors—0
VidLPRO: A Video-Language Pre-training Framework for Robotic and Laparoscopic Surgery—0
Fine-Grained Representation Learning via Multi-Level Contrastive Learning without Class PriorsCode0
Constrained Multi-Layer Contrastive Learning for Implicit Discourse Relationship Recognition—0
Dual-Level Cross-Modal Contrastive ClusteringCode0
Contrastive Learning-based User Identification with Limited Data on Smart Textiles—0
Self-Supervised Contrastive Learning for Videos using Differentiable Local AlignmentCode0
Few-Shot Continual Learning for Activity Recognition in Classroom Surveillance Images—0
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain RecommendationCode0
MOSMOS: Multi-organ segmentation facilitated by medical report supervision—0
SITAR: Semi-supervised Image Transformer for Action Recognition—0
Dual Advancement of Representation Learning and Clustering for Sparse and Noisy ImagesCode0
Towards Generative Class Prompt Learning for Fine-grained Visual RecognitionCode0
LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning—0
MOOSS: Mask-Enhanced Temporal Contrastive Learning for Smooth State Evolution in Visual Reinforcement LearningCode0
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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