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 6101–6125 of 6661 papers

TitleStatusHype
QD-VMR: Query Debiasing with Contextual Understanding Enhancement for Video Moment Retrieval—0
QE-RAG: A Robust Retrieval-Augmented Generation Benchmark for Query Entry Errors—0
QGFace: Quality-Guided Joint Training For Mixed-Quality Face Recognition—0
QontSum: On Contrasting Salient Content for Query-focused Summarization—0
QoS-Aware Graph Contrastive Learning for Web Service Recommendation—0
Quality Assessment of Low Light Restored Images: A Subjective Study and an Unsupervised Model—0
Quality Over Quantity? LLM-Based Curation for a Data-Efficient Audio-Video Foundation Model—0
Quantifying stimulus-relevant representational drift using cross-modality contrastive learning—0
Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization—0
Quantum-Informed Contrastive Learning with Dynamic Mixup Augmentation for Class-Imbalanced Expert Systems—0
Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination—0
Query-based Instance Discrimination Network for Relational Triple Extraction—0
Query-LIFE: Query-aware Language Image Fusion Embedding for E-Commerce Relevance—0
Query Routing for Retrieval-Augmented Language Models—0
RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous Driving with Vision-Language Models—0
RadBERT-CL: Factually-Aware Contrastive Learning For Radiology Report Classification—0
RadZero: Similarity-Based Cross-Attention for Explainable Vision-Language Alignment in Radiology with Zero-Shot Multi-Task Capability—0
RAG4ITOps: A Supervised Fine-Tunable and Comprehensive RAG Framework for IT Operations and Maintenance—0
RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts—0
RAGDiffusion: Faithful Cloth Generation via External Knowledge Assimilation—0
Randomized 3D Scene Generation for Generalizable Self-Supervised Pre-Training—0
Random Walks in Self-supervised Learning for Triangular Meshes—0
Rank Supervised Contrastive Learning for Time Series Classification—0
RCA-NOC: Relative Contrastive Alignment for Novel Object Captioning—0
RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation—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