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 5601–5625 of 6661 papers

TitleStatusHype
Improving Micro-video Recommendation via Contrastive Multiple InterestsCode0
CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning—0
Relation Extraction with Weighted Contrastive Pre-training on Distant SupervisionCode0
A two-steps approach to improve the performance of Android malware detectors—0
Attention-aware contrastive learning for predicting T cell receptor-antigen binding specificity—0
Dynamic Recognition of Speakers for Consent Management by Contrastive Embedding Replay—0
Toward a Geometrical Understanding of Self-supervised Contrastive Learning—0
PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning—0
Arithmetic-Based Pretraining -- Improving Numeracy of Pretrained Language ModelsCode0
Embodied vision for learning object representations—0
SimCPSR: Simple Contrastive Learning for Paper Submission Recommendation SystemCode0
Pre-trained Language Models as Re-Annotators—0
A simple framework for contrastive learning phases of matter—0
Hyperspectral Image Classification With Contrastive Graph Convolutional NetworkCode0
Simple Contrastive Graph Clustering—0
Deep Graph Clustering via Mutual Information Maximization and Mixture Model—0
Transformer-based Cross-Modal Recipe Embeddings with Large Batch Training—0
CoDo: Contrastive Learning with Downstream Background Invariance for Detection—0
Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks—0
Model-Contrastive Learning for Backdoor DefenseCode0
Visual Encoding and Debiasing for CTR Prediction—0
A Closer Look at Few-shot Image Generation—0
Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding—0
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering—0
Relational Representation Learning in Visually-Rich Documents—0
Show:102550
← PrevPage 225 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