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 4426–4450 of 6661 papers

TitleStatusHype
Cross-Modality Learning for Predicting IHC Biomarkers from H&E-Stained Whole-Slide Images—0
CrossMuSim: A Cross-Modal Framework for Music Similarity Retrieval with LLM-Powered Text Description Sourcing and Mining—0
Cross-Patient Pseudo Bags Generation and Curriculum Contrastive Learning for Imbalanced Multiclassification of Whole Slide Image—0
Cross-Platform and Cross-Domain Abusive Language Detection with Supervised Contrastive Learning—0
Cross-Stream Contrastive Learning for Self-Supervised Skeleton-Based Action Recognition—0
Cross-Video Contextual Knowledge Exploration and Exploitation for Ambiguity Reduction in Weakly Supervised Temporal Action Localization—0
CrossVideo: Self-supervised Cross-modal Contrastive Learning for Point Cloud Video Understanding—0
Cross-view Graph Contrastive Representation Learning on Partially Aligned Multi-view Data—0
Cross-View-Prediction: Exploring Contrastive Feature for Hyperspectral Image Classification—0
Cross-view Self-Supervised Learning on Heterogeneous Graph Neural Network via Bootstrapping—0
CryoCCD: Conditional Cycle-consistent Diffusion with Biophysical Modeling for Cryo-EM Synthesis—0
C-SENN: Contrastive Self-Explaining Neural Network—0
CSE-SFP: Enabling Unsupervised Sentence Representation Learning via a Single Forward Pass—0
CSP-AIT-Net: A contrastive learning-enhanced spatiotemporal graph attention framework for short-term metro OD flow prediction with asynchronous inflow tracking—0
CSPCL: Category Semantic Prior Contrastive Learning for Deformable DETR-Based Prohibited Item Detectors—0
CSPM: A Contrastive Spatiotemporal Preference Model for CTR Prediction in On-Demand Food Delivery Services—0
CSTNet: Contrastive Speech Translation Network for Self-Supervised Speech Representation Learning—0
CT-GLIP: 3D Grounded Language-Image Pretraining with CT Scans and Radiology Reports for Full-Body Scenarios—0
CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments—0
CUPR: Contrastive Unsupervised Learning for Person Re-identification—0
CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation—0
CURLing the Dream: Contrastive Representations for World Modeling in Reinforcement Learning—0
Curriculum Learning for Data-Efficient Vision-Language Alignment—0
CustomContrast: A Multilevel Contrastive Perspective For Subject-Driven Text-to-Image Customization—0
Cut the CARP: Fishing for zero-shot story evaluation—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