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 2151–2175 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
FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction—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
Forward-Forward Contrastive Learning—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
Foundation Models for ECG: Leveraging Hybrid Self-Supervised Learning for Advanced Cardiac Diagnostics—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
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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