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 4576–4600 of 6661 papers

TitleStatusHype
Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance LearningCode0
Efficient Document Embeddings via Self-Contrastive Bregman Divergence Learning—0
Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision—0
TabGSL: Graph Structure Learning for Tabular Data Prediction—0
Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression—0
Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization—0
Contrastive Training of Complex-Valued Autoencoders for Object DiscoveryCode0
Clinically Labeled Contrastive Learning for OCT Biomarker Classification—0
Improving Factuality of Abstractive Summarization without Sacrificing Summary QualityCode0
PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification—0
Optimizing Non-Autoregressive Transformers with Contrastive Learning—0
Target-Agnostic Gender-Aware Contrastive Learning for Mitigating Bias in Multilingual Machine TranslationCode0
TaDSE: Template-aware Dialogue Sentence Embeddings—0
Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding—0
Towards Zero-shot Relation Extraction in Web Mining: A Multimodal Approach with Relative XML Path—0
Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality—0
Real-Time Idling Vehicles Detection using Combined Audio-Visual Deep Learning—0
Temporal Contrastive Learning for Spiking Neural Networks—0
Federated Generalized Category Discovery—0
Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning—0
Sentence Representations via Gaussian EmbeddingCode0
DiffAVA: Personalized Text-to-Audio Generation with Visual Alignment—0
Towards Unsupervised Recognition of Token-level Semantic Differences in Related DocumentsCode0
EnSiam: Self-Supervised Learning With Ensemble Representations—0
Transfer-Free Data-Efficient Multilingual Slot Labeling—0
Show:102550
← PrevPage 184 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