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 3451–3500 of 5044 papers

TitleStatusHype
Self-Supervised Pretraining of Graph Neural Network for the Retrieval of Related Mathematical Expressions in Scientific Articles—0
Self-supervised Pretraining of Visual Features in the Wild—0
Self-Supervised Pretraining on Satellite Imagery: a Case Study on Label-Efficient Vehicle Detection—0
Self-supervised pre-training with diffusion model for few-shot landmark detection in x-ray images—0
Self-Supervised Radio Pre-training: Toward Foundational Models for Spectrogram Learning—0
Self-Supervised Radio-Visual Representation Learning for 6G Sensing—0
Self-Supervised Ranking for Representation Learning—0
Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation—0
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised image denoising—0
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised denoising in imaging—0
Self-supervised Regularization for Text Classification—0
Self-Supervised Reinforcement Learning for Recommender Systems—0
Representation Uncertainty in Self-Supervised Learning as Variational Inference—0
Self-supervised Representation Learning for Ultrasound Video—0
Self-Supervised Representation Learning for Visual Anomaly Detection—0
Self-Supervised Representation Learning for Online Handwriting Text Classification—0
Self-Supervised Representation Learning From Multi-Domain Data—0
Self-Supervised Representation Learning from Flow Equivariance—0
Self-Supervised Representation Learning from Temporal Ordering of Automated Driving Sequences—0
Self-Supervised Graph Representation Learning for Neuronal Morphologies—0
Self-supervised representation learning via adaptive hard-positive mining—0
Self-Supervised Representation Learning via Latent Graph Prediction—0
Self-Supervised Representation Learning with Meta Comprehensive Regularization—0
Self-Supervised Representation Learning with Augmentations of Continuous Training Data Improves the Feel and Performance of Myoelectric Control—0
Self-supervised representations in speech-based depression detection—0
Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning—0
Self-Supervised Robust Scene Flow Estimation via the Alignment of Probability Density Functions—0
Self-Supervised Scene Dynamic Recovery from Rolling Shutter Images and Events—0
Self-supervised self-supervision by combining deep learning and probabilistic logic—0
Self-supervised Semantic-driven Phoneme Discovery for Zero-resource Speech Recognition—0
Self-supervised Semantic Segmentation Grounded in Visual Concepts—0
SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking—0
Self-Supervised Shadow Removal—0
Self-Supervised Similarity Learning for Digital Pathology—0
Self-Supervised Spatially Variant PSF Estimation for Aberration-Aware Depth-from-Defocus—0
Self-supervised Spatio-Temporal Graph Mask-Passing Attention Network for Perceptual Importance Prediction of Multi-point Tactility—0
Self-Supervised Speaker Verification with Simple Siamese Network and Self-Supervised Regularization—0
Self-Supervised Structured Representations for Deep Reinforcement Learning—0
Self-supervised Temporal Learning—0
Self-supervised Text-to-SQL Learning with Header Alignment Training—0
Self-Supervised Training of Speaker Encoder with Multi-Modal Diverse Positive Pairs—0
Self-Supervised Training with Autoencoders for Visual Anomaly Detection—0
Self-Supervised Transformer Architecture for Change Detection in Radio Access Networks—0
Self-supervised TransUNet for Ultrasound regional segmentation of the distal radius in children—0
Self-supervised Tumor Segmentation through Layer Decomposition—0
Self-Supervised Versus Supervised Training for Segmentation of Organoid Images—0
Self-supervised Video Instance Segmentation Can Boost Geographic Entity Alignment in Historical Maps—0
Self-supervised video pretraining yields robust and more human-aligned visual representations—0
Self-Supervised Video Representation Learning With Odd-One-Out Networks—0
Self-Supervised Video Representation Learning with Motion-Contrastive Perception—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