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 5951–6000 of 6661 papers

TitleStatusHype
Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic Signals—0
Optimizing E-commerce Search: Toward a Generalizable and Rank-Consistent Pre-Ranking Model—0
Optimizing Non-Autoregressive Transformers with Contrastive Learning—0
Optimizing TD3 for 7-DOF Robotic Arm Grasping: Overcoming Suboptimality with Exploration-Enhanced Contrastive Learning—0
Optimizing transformations for contrastive learning in a differentiable framework—0
Oracle Bone Script Similiar Character Screening Approach Based on Simsiam Contrastive Learning and Supervised Learning—0
Oracle-guided Contrastive Clustering—0
Orientation-Guided Contrastive Learning for UAV-View Geo-Localisation—0
OSAD: Open-Set Aircraft Detection in SAR Images—0
OSCAR-Net: Object-centric Scene Graph Attention for Image Attribution—0
OuroMamba: A Data-Free Quantization Framework for Vision Mamba Models—0
Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks—0
Overcoming the Domain Gap in Contrastive Learning of Neural Action Representations—0
Overfitting In Contrastive Learning?—0
Overlap-Aware Feature Learning for Robust Unsupervised Domain Adaptation for 3D Semantic Segmentation—0
Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management—0
P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding—0
Pair DETR: Contrastive Learning Speeds Up DETR Training—0
Pair-Level Supervised Contrastive Learning for Natural Language Inference—0
PALI at SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts—0
ParamCrop: Parametric Cubic Cropping for Video Contrastive Learning—0
Segmentation of Parotid Gland Tumors Using Multimodal MRI and Contrastive Learning—0
Part123: Part-aware 3D Reconstruction from a Single-view Image—0
PARTFIELD: Learning 3D Feature Fields for Part Segmentation and Beyond—0
Partial Federated Learning—0
Partial Multi-View Clustering via Meta-Learning and Contrastive Feature Alignment—0
Partial Symmetry Detection for 3D Geometry using Contrastive Learning with Geodesic Point Cloud Patches—0
Partitioning Image Representation in Contrastive Learning—0
PartMix: Regularization Strategy to Learn Part Discovery for Visible-Infrared Person Re-identification—0
Patch-Wise Hypergraph Contrastive Learning with Dual Normal Distribution Weighting for Multi-Domain Stain Transfer—0
SRA: A Novel Method to Improve Feature Embedding in Self-supervised Learning for Histopathological Images—0
Patient Domain Supervised Contrastive Learning for Lung Sound Classification Using Mobile Phone—0
Pay Attention to the Foreground in Object-Centric Learning—0
Paying Alignment Tax with Contrastive Learning—0
Unsupervised Deep Representation Learning and Few-Shot Classification of PolSAR Images—0
PCPs: Patient Cardiac Prototypes—0
PDT: Pretrained Dual Transformers for Time-aware Bipartite Graphs—0
PeakNetFP: Peak-based Neural Audio Fingerprinting Robust to Extreme Time Stretching—0
Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusion—0
Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints—0
Pedestrian Detection by Exemplar-Guided Contrastive Learning—0
PepDoRA: A Unified Peptide Language Model via Weight-Decomposed Low-Rank Adaptation—0
PepGB: Facilitating peptide drug discovery via graph neural networks—0
Perceptual Inductive Bias Is What You Need Before Contrastive Learning—0
PerCoNet: News Recommendation with Explicit Persona and Contrastive Learning—0
PersonaBooth: Personalized Text-to-Motion Generation—0
Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach—0
Modeling Adaptive Fine-grained Task Relatedness for Joint CTR-CVR Estimation—0
Personalized News Recommendation System via LLM Embedding and Co-Occurrence Patterns—0
Personalized Prompt for Sequential Recommendation—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