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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4151–4200 of 5044 papers

TitleStatusHype
Saliency Can Be All You Need In Contrastive Self-Supervised Learning—0
Saliency Guided Contrastive Learning on Scene Images—0
SALTTS: Leveraging Self-Supervised Speech Representations for improved Text-to-Speech Synthesis—0
SAM-dPCR: Real-Time and High-throughput Absolute Quantification of Biological Samples Using Zero-Shot Segment Anything Model—0
Sample-efficient Adversarial Imitation Learning—0
Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans—0
SanitAIs: Unsupervised Data Augmentation to Sanitize Trojaned Neural Networks—0
SARF: Aliasing Relation Assisted Self-Supervised Learning for Few-shot Relation Reasoning—0
SAR Object Detection with Self-Supervised Pretraining and Curriculum-Aware Sampling—0
SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer—0
SA-WavLM: Speaker-Aware Self-Supervised Pre-training for Mixture Speech—0
SB-SSL: Slice-Based Self-Supervised Transformers for Knee Abnormality Classification from MRI—0
Scalable Deep Graph Clustering with Random-walk based Self-supervised Learning—0
Scalable Graph Self-Supervised Learning—0
LPNL: Scalable Link Prediction with Large Language Models—0
Scaling 4D Representations—0
SCDNet: Self-supervised Learning Feature-based Speaker Change Detection—0
SCE-MAE: Selective Correspondence Enhancement with Masked Autoencoder for Self-Supervised Landmark Estimation—0
Scene Flow from Point Clouds with or without Learning—0
Score-based Diffusion Models With Self-supervised Learning For Accelerated 3D Multi-contrast Cardiac Magnetic Resonance Imaging—0
Score-based Self-supervised MRI Denoising—0
Screener: Self-supervised Pathology Segmentation Model for 3D Medical Images—0
SCVRL: Shuffled Contrastive Video Representation Learning—0
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring—0
Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions—0
Seeing the Whole in the Parts in Self-Supervised Representation Learning—0
Seeing voices and hearing voices: learning discriminative embeddings using cross-modal self-supervision—0
Visual Self-supervised Learning Scheme for Dense Prediction Tasks on X-ray Images—0
SEL-CIE: Knowledge-Guided Self-Supervised Learning Framework for CIE-XYZ Reconstruction from Non-Linear sRGB Images—0
Selecting task with optimal transport self-supervised learning for few-shot classification—0
SelectTTS: Synthesizing Anyone's Voice via Discrete Unit-Based Frame Selection—0
Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series—0
Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction—0
Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning—0
Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification—0
Self-Distilled Representation Learning for Time Series—0
Self-Ensemling for 3D Point Cloud Domain Adaption—0
SelfFed: Self-supervised Federated Learning for Data Heterogeneity and Label Scarcity in IoMT—0
Self-GenomeNet: Self-supervised Learning with Reverse-Complement Context Prediction for Nucleotide-level Genomics Data—0
Self-Improving Visual Odometry—0
Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images—0
SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning—0
SelfME: Self-Supervised Motion Learning for Micro-Expression Recognition—0
Self-MI: Efficient Multimodal Fusion via Self-Supervised Multi-Task Learning with Auxiliary Mutual Information Maximization—0
Self-Point-Flow: Self-Supervised Scene Flow Estimation from Point Clouds with Optimal Transport and Random Walk—0
SelfReplay: Adapting Self-Supervised Sensory Models via Adaptive Meta-Task Replay—0
Self-Score: Self-Supervised Learning on Score-Based Models for MRI Reconstruction—0
Self Semi Supervised Neural Architecture Search for Semantic Segmentation—0
Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis—0
Self-Supervised 3D Monocular Object Detection by Recycling Bounding Boxes—0
Show:102550
← PrevPage 84 of 101Next →
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