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 3176–3200 of 6661 papers

TitleStatusHype
ETSCL: An Evidence Theory-Based Supervised Contrastive Learning Framework for Multi-modal Glaucoma GradingCode0
Self-Supervised Video Representation Learning in a Heuristic Decoupled Perspective—0
LIDIA: Precise Liver Tumor Diagnosis on Multi-Phase Contrast-Enhanced CT via Iterative Fusion and Asymmetric Contrastive Learning—0
Keypoint Aware Masked Image ModellingCode0
X-Former: Unifying Contrastive and Reconstruction Learning for MLLMs—0
CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering—0
Causality-inspired Discriminative Feature Learning in Triple Domains for Gait Recognition—0
Mutual Information Guided Optimal Transport for Unsupervised Visible-Infrared Person Re-identification—0
Subgraph-Aware Training of Language Models for Knowledge Graph Completion Using Structure-Aware Contrastive LearningCode0
Continual Learning for Temporal-Sensitive Question Answering—0
Cycle Contrastive Adversarial Learning for Unsupervised image Deraining—0
Overfitting In Contrastive Learning?—0
SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks—0
An efficient framework based on large foundation model for cervical cytopathology whole slide image screeningCode0
EndoFinder: Online Image Retrieval for Explainable Colorectal Polyp Diagnosis—0
Adaptive Patch Contrast for Weakly Supervised Semantic Segmentation—0
Towards Robust Event-based Networks for Nighttime via Unpaired Day-to-Night Event TranslationCode0
A Self-Supervised Learning Pipeline for Demographically Fair Facial Attribute Classification—0
Cross-Lingual Multi-Hop Knowledge Editing -- Benchmarks, Analysis and a Simple Contrastive Learning based Approach—0
Towards Robust Recommendation via Decision Boundary-aware Graph Contrastive Learning—0
Is Contrasting All You Need? Contrastive Learning for the Detection and Attribution of AI-generated Text—0
Heterogeneous Subgraph Network with Prompt Learning for Interpretable Depression Detection on Social Media—0
Guidelines for Augmentation Selection in Contrastive Learning for Time Series ClassificationCode0
One Stone, Four Birds: A Comprehensive Solution for QA System Using Supervised Contrastive LearningCode0
Full-Stage Pseudo Label Quality Enhancement for Weakly-supervised Temporal Action LocalizationCode0
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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