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 4351–4375 of 6661 papers

TitleStatusHype
Contrastive random lead coding for channel-agnostic self-supervision of biosignals—0
Contrastive Rendering for Ultrasound Image Segmentation—0
Contrastive Representation Learning: A Framework and Review—0
Contrastive Representation Learning Based on Multiple Node-centered Subgraphs—0
Contrastive Representation Learning for Acoustic Parameter Estimation—0
Contrastive Representation Learning for 3D Protein Structures—0
Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities—0
Contrastive Representation Learning for Hand Shape Estimation—0
Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence—0
Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections—0
Contrastive Self-Supervised Learning As Neural Manifold Packing—0
Contrastive Self-Supervised Learning for Spatio-Temporal Analysis of Lung Ultrasound Videos—0
Contrastive Semi-Supervised Learning for 2D Medical Image Segmentation—0
Contrastive Sequential Interaction Network Learning on Co-Evolving Riemannian Spaces—0
Contrastive Similarity Learning for Market Forecasting: The ContraSim Framework—0
Contrastive Speaker-Aware Learning for Multi-party Dialogue Generation with LLMs—0
Contrastive Speaker Embedding With Sequential Disentanglement—0
Contrastive String Representation Learning using Synthetic Data—0
Contrastive Unsupervised Learning for Audio Fingerprinting—0
Contrastive Video-Language Segmentation—0
Contrastive Video Textures—0
Contrastive View Design Strategies to Enhance Robustness to Domain Shifts in Downstream Object Detection—0
Contrastive Weighted Learning for Near-Infrared Gaze Estimation—0
ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale LiDAR Point Clouds—0
ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER—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