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 6076–6100 of 6661 papers

TitleStatusHype
Improving Tail-Class Representation with Centroid Contrastive Learning—0
Momentum Contrastive Autoencoder: Using Contrastive Learning for Latent Space Distribution Matching in WAE—0
Contrastive Learning of Visual-Semantic Embeddings—0
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning—0
Towards EEG signals codification using contrastiveloss—0
Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding—0
Self-supervised Contrastive Attributed Graph Clustering—0
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in scienceCode0
Rethinking Self-Supervision Objectives for Generalizable Coherence Modeling—0
Sentence-aware Contrastive Learning for Open-Domain Passage Retrieval—0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation—0
False Negative Distillation and Contrastive Learning for Personalized Outfit Recommendation—0
OPEn: An Open-ended Physics Environment for Learning Without a Task—0
The Impact of Spatiotemporal Augmentations on Self-Supervised Audiovisual Representation Learning—0
Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning—0
Unsupervised Contrastive Learning with Simple Transformation for 3D Point Cloud Data—0
Contrastive Learning Through Time—0
SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health RecordsCode0
Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition—0
Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization—0
Contrastive String Representation Learning using Synthetic Data—0
SCaLa: Supervised Contrastive Learning for End-to-End Speech Recognition—0
Towards Learning (Dis)-Similarity of Source Code from Program Contrasts—0
MC-LCR: Multi-modal contrastive classification by locally correlated representations for effective face forgery detection—0
Using Contrastive Learning and Pseudolabels to learn representations for Retail Product Image Classification—0
Show:102550
← PrevPage 244 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