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 4401–4425 of 6661 papers

TitleStatusHype
Learning beyond sensations: how dreams organize neuronal representations—0
COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery DetectionCode0
ADS-Cap: A Framework for Accurate and Diverse Stylized Captioning with Unpaired Stylistic CorporaCode0
ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders—0
Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach—0
Three Factors to Improve Out-of-Distribution Detection—0
Orientation-Guided Contrastive Learning for UAV-View Geo-Localisation—0
A Multi-Source Data Fusion-based Semantic Segmentation Model for Relic Landslide Detection—0
Dynamically Scaled Temperature in Self-Supervised Contrastive LearningCode0
Graph Contrastive Learning with Generative Adversarial Network—0
Center Contrastive Loss for Metric Learning—0
Graph Embedding Dynamic Feature-based Supervised Contrastive Learning of Transient Stability for Changing Power Grid Topologies—0
Self-Supervised Contrastive BERT Fine-tuning for Fusion-based Reviewed-Item RetrievalCode0
Contrastive Learning for API Aspect AnalysisCode0
Can Self-Supervised Representation Learning Methods Withstand Distribution Shifts and Corruptions?Code0
Contrastive Conditional Latent Diffusion for Audio-visual SegmentationCode0
C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation—0
Self-Supervised Learning of Gait-Based Biomarkers—0
Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Images—0
MUSE: Multi-View Contrastive Learning for Heterophilic Graphs—0
BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection—0
Self-Supervised Graph Transformer for Deepfake Detection—0
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing—0
G2L: Semantically Aligned and Uniform Video Grounding via Geodesic and Game Theory—0
Towards multi-modal anatomical landmark detection for ultrasound-guided brain tumor resection with contrastive learning—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