SOTAVerified

Self-Supervised Learning

Self-Supervised Learning is proposed for utilizing unlabeled data with the success of supervised learning. Producing a dataset with good labels is expensive, while unlabeled data is being generated all the time. The motivation of Self-Supervised Learning is to make use of the large amount of unlabeled data. The main idea of Self-Supervised Learning is to generate the labels from unlabeled data, according to the structure or characteristics of the data itself, and then train on this unsupervised data in a supervised manner. Self-Supervised Learning is wildly used in representation learning to make a model learn the latent features of the data. This technique is often employed in computer vision, video processing and robot control.

Source: Self-supervised Point Set Local Descriptors for Point Cloud Registration

Image source: LeCun

Papers

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Showing 4801–4850 of 5044 papers

TitleStatusHype
Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars—0
Self-supervised Learning for Enhancing Geometrical Modeling in 3D-Aware Generative Adversarial Network—0
Self-supervised learning for fast and scalable time series hyper-parameter tuning—0
Self Supervised Learning for Few Shot Hyperspectral Image Classification—0
Self-Supervised Learning for Gastritis Detection with Gastric X-ray Images—0
Self-supervised Learning for Gastrointestinal Pathologies Endoscopy Image Classification with Triplet Loss—0
Self-supervised Learning for Geospatial AI: A Survey—0
Self-Supervised Learning for Group Equivariant Neural Networks—0
Self-supervised Learning for Heterogeneous Graph via Structure Information based on Metapath—0
Self-supervised learning for hotspot detection and isolation from thermal images—0
Self-Supervised Learning for Identifying Defects in Sewer Footage—0
Self-Supervised Learning for Image Segmentation: A Comprehensive Survey—0
Self-Supervised Learning for Improved Synthetic Aperture Sonar Target Recognition—0
Self-supervised learning for infant cry analysis—0
Self-Supervised Learning for Interactive Perception of Surgical Thread for Autonomous Suture Tail-Shortening—0
Self-Supervised Learning for Interventional Image Analytics: Towards Robust Device Trackers—0
Self-Supervised Learning for Invariant Representations from Multi-Spectral and SAR Images—0
Semi-supervised learning for joint SAR and multispectral land cover classification—0
Self-supervised Learning for Label Sparsity in Computational Drug Repositioning—0
Self-Supervised Learning for Large-Scale Preventive Security Constrained DC Optimal Power Flow—0
Self-Supervised Learning for Medical Image Data with Anatomy-Oriented Imaging Planes—0
Self-Supervised Learning for Modeling Gamma-ray Variability in Blazars—0
Self-Supervised learning for Neural Architecture Search (NAS)—0
Self-Supervised Learning for Biological Sample Localization in 3D Tomographic Images—0
Self-Supervised Learning for Ordered Three-Dimensional Structures—0
Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification—0
Self-Supervised Learning for Pairwise Data Refinement—0
Selfsupervised learning for pathological speech detection—0
Efficient Personalized Speech Enhancement through Self-Supervised Learning—0
Self-Supervised Learning for Physiologically-Based Pharmacokinetic Modeling in Dynamic PET—0
Self-Supervised Learning for Point Clouds Data: A Survey—0
Self-Supervised Learning for Pre-training Capsule Networks: Overcoming Medical Imaging Dataset Challenges—0
Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey—0
Self-Supervised Learning for Real-World Object Detection: a Survey—0
Self-Supervised Learning for Robotic Leaf Manipulation: A Hybrid Geometric-Neural Approach—0
Self-supervised learning for robust voice cloning—0
Self-Supervised Learning for Segmentation—0
Self-supervised Learning for Semi-supervised Temporal Language Grounding—0
Self-supervised Learning for Sequential Recommendation with Model Augmentation—0
Self-supervised Learning for Single View Depth and Surface Normal Estimation—0
Self-Supervised Learning for Spinal MRIs—0
Self-Supervised Learning for Stereo Reconstruction on Aerial Images—0
Self-Supervised Learning for Stereo Matching with Self-Improving Ability—0
Self-Supervised Learning for Text Recognition: A Critical Survey—0
Self-supervised Learning for Unintentional Action Prediction—0
Self-Supervised Learning for User Localization—0
Self-Supervised Learning for Visual Summary Identification in Scientific Publications—0
Self-Supervised Learning for WiFi CSI-Based Human Activity Recognition: A Systematic Study—0
Self-supervised Learning from 100 Million Medical Images—0
Self-Supervised Learning from Semantically Imprecise Data—0
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All datasetsDABSSTL-10CIFAR10cifar100ImageNet-100 (TEMI Split)TinyImageNetCIFAR-10CIFAR-100CREMA-DTiny ImageNet

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pretraining: NoneImages & Text57.5—Unverified
2Pretraining: ShEDImages & Text54.3—Unverified
3Pretraining: e-MixImages & Text48.9—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50Accuracy91.7—Unverified
2ResNet18Accuracy91.02—Unverified
3MV-MRAccuracy89.67—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy93.89—Unverified
2ResNet18average top-1 classification accuracy92.58—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy72.51—Unverified
2ResNet18average top-1 classification accuracy69.31—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy82.64—Unverified
2CorInfomax (ResNet18)Top-1 Accuracy80.48—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy51.84—Unverified
2ResNet18average top-1 classification accuracy51.67—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy93.18—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy71.61—Unverified
#ModelMetricClaimedVerifiedStatus
1Hybrid BYOL-S/CvTAccuracy67.2—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy54.86—Unverified