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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4751–4775 of 6661 papers

TitleStatusHype
Enhancing medical vision-language contrastive learning via inter-matching relation modelling—0
Enhancing Multimodal Affective Analysis with Learned Live Comment Features—0
Enhancing Multimodal Entity Linking with Jaccard Distance-based Conditional Contrastive Learning and Contextual Visual Augmentation—0
Enhancing Persona Consistency for LLMs' Role-Playing using Persona-Aware Contrastive Learning—0
Enhancing Post-training Quantization Calibration through Contrastive Learning—0
CoCoSoDa: Effective Contrastive Learning for Code Search—0
Enhancing Sequential Recommendation with Graph Contrastive Learning—0
Enhancing Social Relation Inference with Concise Interaction Graph and Discriminative Scene Representation—0
Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy—0
Enhancing the Nonlinear Mutual Dependencies in Transformers with Mutual Information—0
Investigating the Role of Attribute Context in Vision-Language Models for Object Recognition and Detection—0
Enhancing the Unified Streaming and Non-streaming Model with Contrastive Learning—0
Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning—0
Enhancing Travel Decision-Making: A Contrastive Learning Approach for Personalized Review Rankings in Accommodations—0
Enhancing Visual Document Understanding with Contrastive Learning in Large Visual-Language Models—0
Enhancing Whole Slide Image Classification through Supervised Contrastive Domain Adaptation—0
Enriching Location Representation with Detailed Semantic Information—0
EnSiam: Self-Supervised Learning With Ensemble Representations—0
EntityCLIP: Entity-Centric Image-Text Matching via Multimodal Attentive Contrastive Learning—0
Entity-level Factual Adaptiveness of Fine-tuning based Abstractive Summarization Models—0
EPContrast: Effective Point-level Contrastive Learning for Large-scale Point Cloud Understanding—0
Episodes Discovery Recommendation with Multi-Source Augmentations—0
Episodic-free Task Selection for Few-shot Learning—0
Episodic Novelty Through Temporal Distance—0
ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation—0
Show:102550
← PrevPage 191 of 267Next →
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