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 4201–4225 of 6661 papers

TitleStatusHype
Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure—0
Contrastive Knowledge Graph Error DetectionCode0
Improving Pixel-Level Contrastive Learning by Leveraging Exogenous Depth Information—0
The Runner-up Solution for YouTube-VIS Long Video Challenge 2022—0
Contrastive Positive Sample Propagation along the Audio-Visual Event LineCode1
FedSiam-DA: Dual-aggregated Federated Learning via Siamese Network under Non-IID Data—0
ProtSi: Prototypical Siamese Network with Data Augmentation for Few-Shot Subjective Answer EvaluationCode0
Self-Supervised Visual Representation Learning via Residual Momentum—0
Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel SemanticsCode1
Mitigating Urban-Rural Disparities in Contrastive Representation Learning with Satellite ImageryCode0
Anomaly Detection via Multi-Scale Contrasted Memory—0
An Efficient COarse-to-fiNE Alignment Framework @ Ego4D Natural Language Queries Challenge 2022—0
Learning Reward Functions for Robotic Manipulation by Observing Humans—0
Keep Your Friends Close & Enemies Farther: Debiasing Contrastive Learning with Spatial Priors in 3D Radiology Images—0
Masked Reconstruction Contrastive Learning with Information Bottleneck Principle—0
Unveiling interpretable development-specific gene signatures in the developing human prefrontal cortex with ICGSCode0
Region Embedding with Intra and Inter-View Contrastive LearningCode0
Unsupervised Feature Clustering Improves Contrastive Representation Learning for Medical Image Segmentation—0
DeepRGVP: A Novel Microstructure-Informed Supervised Contrastive Learning Framework for Automated Identification Of The Retinogeniculate Pathway Using dMRI Tractography—0
Improved disentangled speech representations using contrastive learning in factorized hierarchical variational autoencoder—0
CorruptEncoder: Data Poisoning based Backdoor Attacks to Contrastive LearningCode1
Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment PredictionCode0
False: False Negative Samples Aware Contrastive Learning for Semantic Segmentation of High-Resolution Remote Sensing ImageCode1
RankCSE: Unsupervised Representation Learning via Learning to RankCode1
Generative Aspect-Based Sentiment Analysis with Contrastive Learning and Expressive StructureCode1
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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