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 2151–2200 of 6661 papers

TitleStatusHype
Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning—0
FRET: Feature Redundancy Elimination for Test Time Adaptation—0
A Theoretical Study of Inductive Biases in Contrastive Learning—0
Detecting Anomalies Through Contrast in Heterogeneous Data—0
A Cross-Corpus Speech Emotion Recognition Method Based on Supervised Contrastive Learning—0
A theoretical framework for self-supervised contrastive learning for continuous dependent data—0
CLLMFS: A Contrastive Learning enhanced Large Language Model Framework for Few-Shot Named Entity Recognition—0
AI, Entrepreneurs, and Privacy: Deep Learning Outperforms Humans in Detecting Entrepreneurs from Image Data—0
CLLD: Contrastive Learning with Label Distance for Text Classification—0
From Real Artifacts to Virtual Reference: A Robust Framework for Translating Endoscopic Images—0
A Theoretical Analysis of Contrastive Unsupervised Representation Learning—0
CL-ISR: A Contrastive Learning and Implicit Stance Reasoning Framework for Misleading Text Detection on Social Media—0
A Multi-Source Data Fusion-based Semantic Segmentation Model for Relic Landslide Detection—0
DER-GCN: Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialogue Emotion Recognition—0
Depth-CUPRL: Depth-Imaged Contrastive Unsupervised Prioritized Representations in Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehicles—0
CLIP-S4: Language-Guided Self-Supervised Semantic Segmentation—0
CLIP-S^4: Language-Guided Self-Supervised Semantic Segmentation—0
Athena: Safe Autonomous Agents with Verbal Contrastive Learning—0
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach—0
FSCIL-SEI: Few-Shot Class-Incremental Learning Approach for Specific Emitter Identification—0
Density-Guided Semi-Supervised 3D Semantic Segmentation with Dual-Space Hardness Sampling—0
Dense Semantic Contrast for Self-Supervised Visual Representation Learning—0
CLIP-PING: Boosting Lightweight Vision-Language Models with Proximus Intrinsic Neighbors Guidance—0
Dense Contrastive Visual-Linguistic Pretraining—0
CLIPose: Category-Level Object Pose Estimation with Pre-trained Vision-Language Knowledge—0
A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches—0
Denoising Multi-modal Sequential Recommenders with Contrastive Learning—0
CLIP-Lung: Textual Knowledge-Guided Lung Nodule Malignancy Prediction—0
Denoising Long- and Short-term Interests for Sequential Recommendation—0
A Hybrid Egocentric Activity Anticipation Framework via Memory-Augmented Recurrent and One-Shot Representation Forecasting—0
A Cross Branch Fusion-Based Contrastive Learning Framework for Point Cloud Self-supervised Learning—0
A Tale of Two Classes: Adapting Supervised Contrastive Learning to Binary Imbalanced Datasets—0
A Hybrid CNN-Transformer Architecture with Frequency Domain Contrastive Learning for Image Deraining—0
Del Visual al Auditivo: Sonorización de Escenas Guiada por Imagen—0
CLIP-Hand3D: Exploiting 3D Hand Pose Estimation via Context-Aware Prompting—0
AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction—0
MMPKUBase: A Comprehensive and High-quality Chinese Multi-modal Knowledge Graph—0
From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing—0
Delving into E-Commerce Product Retrieval with Vision-Language Pre-training—0
3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations—0
DEHRFormer: Real-time Transformer for Depth Estimation and Haze Removal from Varicolored Haze Scenes—0
CLIP-FLow: Contrastive Learning by semi-supervised Iterative Pseudo labeling for Optical Flow Estimation—0
From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension—0
Defending Multimodal Backdoored Models by Repulsive Visual Prompt Tuning—0
CLIP-Driven Open-Vocabulary 3D Scene Graph Generation via Cross-Modality Contrastive Learning—0
Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Losses—0
CLIPC8: Face liveness detection algorithm based on image-text pairs and contrastive learning—0
A Hybrid Approach for Document Layout Analysis in Document images—0
From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning—0
Deep Temporal Contrastive Clustering—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