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 551–600 of 6661 papers

TitleStatusHype
O1 Embedder: Let Retrievers Think Before Action—0
Refine Knowledge of Large Language Models via Adaptive Contrastive Learning—0
Learning Clustering-based Prototypes for Compositional Zero-shot LearningCode1
Multimodal Task Representation Memory Bank vs. Catastrophic Forgetting in Anomaly Detection—0
Structure-preserving contrastive learning for spatial time seriesCode0
Unleashing the Potential of Pre-Trained Diffusion Models for Generalizable Person Re-IdentificationCode0
RAMer: Reconstruction-based Adversarial Model for Multi-party Multi-modal Multi-label Emotion RecognitionCode0
Group Reasoning Emission Estimation Networks—0
Learning Street View Representations with Spatiotemporal ContrastCode0
Self-Supervised Learning for Pre-training Capsule Networks: Overcoming Medical Imaging Dataset Challenges—0
Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE—0
Graph Contrastive Learning for Connectome ClassificationCode0
Learning Temporal Invariance in Android Malware Detectors—0
Neuron Platonic Intrinsic Representation From Dynamics Using Contrastive Learning—0
Adaptive Margin Contrastive Learning for Ambiguity-aware 3D Semantic Segmentation—0
Boosting Knowledge Graph-based Recommendations through Confidence-Aware Augmentation with Large Language Models—0
Consistency of augmentation graph and network approximability in contrastive learningCode0
FactorGCL: A Hypergraph-Based Factor Model with Temporal Residual Contrastive Learning for Stock Returns Prediction—0
Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion—0
TopoCL: Topological Contrastive Learning for Time Series—0
RLOMM: An Efficient and Robust Online Map Matching Framework with Reinforcement Learning—0
SLCGC: A lightweight Self-supervised Low-pass Contrastive Graph Clustering Network for Hyperspectral Images—0
Hierarchical Consensus Network for Multiview Feature LearningCode1
Boundary-Driven Table-Filling with Cross-Granularity Contrastive Learning for Aspect Sentiment Triplet Extraction—0
Rotation-Adaptive Point Cloud Domain Generalization via Intricate Orientation Learning—0
Mosaic3D: Foundation Dataset and Model for Open-Vocabulary 3D Segmentation—0
T-SCEND: Test-time Scalable MCTS-enhanced Diffusion ModelCode1
Efficient Domain Adaptation of Multimodal Embeddings using Constrastive Learning—0
Mask-informed Deep Contrastive Incomplete Multi-view ClusteringCode0
Multi-level Supervised Contrastive Learning—0
VisTA: Vision-Text Alignment Model with Contrastive Learning using Multimodal Data for Evidence-Driven, Reliable, and Explainable Alzheimer's Disease Diagnosis—0
Provable Ordering and Continuity in Vision-Language Pretraining for Generalizable Embodied AgentsCode0
A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis—0
BC-GAN: A Generative Adversarial Network for Synthesizing a Batch of Collocated Clothing—0
General Feature Extraction In SAR Target Classification: A Contrastive Learning Approach Across Sensor TypesCode0
RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning—0
neuro2voc: Decoding Vocalizations from Neural ActivityCode0
CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive LearningCode1
Contrastive Forward-Forward: A Training Algorithm of Vision Transformer—0
Prostate-Specific Foundation Models for Enhanced Detection of Clinically Significant CancerCode1
Improving vision-language alignment with graph spiking hybrid Networks—0
DyPCL: Dynamic Phoneme-level Contrastive Learning for Dysarthric Speech Recognition—0
Improving Multi-Label Contrastive Learning by Leveraging Label Distribution—0
ReactEmbed: A Cross-Domain Framework for Protein-Molecule Representation Learning via Biochemical Reaction NetworksCode0
A Learnable Multi-views Contrastive Framework with Reconstruction Discrepancy for Medical Time-Series—0
Contrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A Comprehensive Graph Representation Framework from Local to GlobalCode0
Sebra: Debiasing Through Self-Guided Bias RankingCode0
IROAM: Improving Roadside Monocular 3D Object Detection Learning from Autonomous Vehicle Data Domain—0
Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features—0
Deconstruct Complexity (DeComplex): A Novel Perspective on Tackling Dense Action Detection—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