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 4001–4050 of 6661 papers

TitleStatusHype
CheX-Nomaly: Segmenting Lung Abnormalities from Chest Radiographs using Machine Learning—0
CHGNN: A Semi-Supervised Contrastive Hypergraph Learning Network—0
CHIME: A Compressive Framework for Holistic Interest Modeling—0
CHOSEN: Contrastive Hypothesis Selection for Multi-View Depth Refinement—0
CIBR: Cross-modal Information Bottleneck Regularization for Robust CLIP Generalization—0
CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning—0
CILDA: Contrastive Data Augmentation using Intermediate Layer Knowledge Distillation—0
CILF:Causality Inspired Learning Framework for Out-of-Distribution Vehicle Trajectory Prediction—0
Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity—0
CIPER: Combining Invariant and Equivariant Representations Using Contrastive and Predictive Learning—0
CitationSum: Citation-aware Graph Contrastive Learning for Scientific Paper Summarization—0
CI w/o TN: Context Injection without Task Name for Procedure Planning—0
CktGen: Specification-Conditioned Analog Circuit Generation—0
CL3DOR: Contrastive Learning for 3D Large Multimodal Models via Odds Ratio on High-Resolution Point Clouds—0
CLAF: Contrastive Learning with Augmented Features for Imbalanced Semi-Supervised Learning—0
ClamNet: Using contrastive learning with variable depth Unets for medical image segmentation—0
CLAMP: Contrastive LAnguage Model Prompt-tuning—0
Self-supervised New Activity Detection in Sensor-based Smart Environments—0
CLAR: Contrastive Learning of Auditory Representations—0
ClarityEthic: Explainable Moral Judgment Utilizing Contrastive Ethical Insights from Large Language Models—0
CLaSP: Learning Concepts for Time-Series Signals from Natural Language Supervision—0
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels—0
Class-aware and Augmentation-free Contrastive Learning from Label Proportion—0
Classes Are Not Equal: An Empirical Study on Image Recognition Fairness—0
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks—0
Classification and Clustering of Sentence-Level Embeddings of Scientific Articles Generated by Contrastive Learning—0
Classification of Mild Cognitive Impairment Based on Dynamic Functional Connectivity Using Spatio-Temporal Transformer—0
Classification of Seeds using Domain Randomization on Self-Supervised Learning Frameworks—0
Class Instance Balanced Learning for Long-Tailed Classification—0
Class Prototypes Based Contrastive Learning for Classifying Multi-Label and Fine-Grained Educational Videos—0
Class Relationship Embedded Learning for Source-Free Unsupervised Domain Adaptation—0
CLAWS: Contrastive Learning with hard Attention and Weak Supervision—0
Contrastive Representation Disentanglement for Clustering—0
CLCE: An Approach to Refining Cross-Entropy and Contrastive Learning for Optimized Learning Fusion—0
CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data—0
CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation—0
CLDA-YOLO: Visual Contrastive Learning Based Domain Adaptive YOLO Detector—0
CLDTA: Contrastive Learning based on Diagonal Transformer Autoencoder for Cross-Dataset EEG Emotion Recognition—0
CLEAR: Contrastive Learning for Sentence Representation—0
CLeaRForecast: Contrastive Learning of High-Purity Representations for Time Series Forecasting—0
ClearVision: Leveraging CycleGAN and SigLIP-2 for Robust All-Weather Classification in Traffic Camera Imagery—0
CLEP-GAN: An Innovative Approach to Subject-Independent ECG Reconstruction from PPG Signals—0
CLERF: Contrastive LEaRning for Full Range Head Pose Estimation—0
CLExtract: Recovering Highly Corrupted DVB/GSE Satellite Stream with Contrastive Learning—0
CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection—0
CLGNN: A Contrastive Learning-based GNN Model for Betweenness Centrality Prediction on Temporal Graphs—0
Click-through Rate Prediction with Auto-Quantized Contrastive Learning—0
CLICv2: Image Complexity Representation via Content Invariance Contrastive Learning—0
Generalizable Denoising of Microscopy Images using Generative Adversarial Networks and Contrastive Learning—0
Clinical Contrastive Learning for Biomarker 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