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 2301–2350 of 5044 papers

TitleStatusHype
Objectives Matter: Understanding the Impact of Self-Supervised Objectives on Vision Transformer Representations—0
Self-Supervised Multi-Object Tracking For Autonomous Driving From Consistency Across Timescales—0
Learning imaging mechanism directly from optical microscopy observations—0
Zero-shot text-to-speech synthesis conditioned using self-supervised speech representation model—0
A Cookbook of Self-Supervised Learning—0
End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch—0
Self-supervised Learning by View Synthesis—0
Learning Symbolic Representations Through Joint GEnerative and DIscriminative Training—0
A vector quantized masked autoencoder for speech emotion recognitionCode1
A Revisit of the Normalized Eight-Point Algorithm and A Self-Supervised Deep Solution—0
Deep Metric Learning Assisted by Intra-variance in A Semi-supervised View of Learning—0
Complex Mixer for MedMNIST Classification Decathlon—0
SARF: Aliasing Relation Assisted Self-Supervised Learning for Few-shot Relation Reasoning—0
DocMAE: Document Image Rectification via Self-supervised Representation LearningCode1
DCELANM-Net:Medical Image Segmentation based on Dual Channel Efficient Layer Aggregation Network with Learner—0
Shuffle & Divide: Contrastive Learning for Long Text—0
ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision—0
CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image UnderstandingCode1
To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review—0
Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging—0
DAS-N2N: Machine learning Distributed Acoustic Sensing (DAS) signal denoising without clean dataCode1
Morph-SSL: Self-Supervision with Longitudinal Morphing to Predict AMD Progression from OCT—0
Self-Supervised Learning from Non-Object Centric Images with a Geometric Transformation Sensitive ArchitectureCode0
BenchMD: A Benchmark for Unified Learning on Medical Images and SensorsCode1
Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection—0
Multimodal Representation Learning of Cardiovascular Magnetic Resonance Imaging—0
A CTC Alignment-based Non-autoregressive Transformer for End-to-end Automatic Speech Recognition—0
Multi-View Graph Representation Learning Beyond HomophilyCode0
Self-supervised Auxiliary Loss for Metric Learning in Music Similarity-based Retrieval and Auto-tagging—0
Explaining, Analyzing, and Probing Representations of Self-Supervised Learning Models for Sensor-based Human Activity Recognition—0
Self-Supervised Scene Dynamic Recovery from Rolling Shutter Images and Events—0
Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial LidarCode2
Self-Supervised Learning based Depth Estimation from Monocular ImagesCode0
In-Distribution and Out-of-Distribution Self-supervised ECG Representation Learning for Arrhythmia Detection—0
Multi-Mode Online Knowledge Distillation for Self-Supervised Visual Representation LearningCode1
Enhancing Self-Supervised Learning for Remote Sensing with Elevation Data: A Case Study with Scarce And High Level Semantic LabelsCode0
Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph—0
A surprisingly simple technique to control the pretraining bias for better transfer: Expand or Narrow your representation—0
Semi-Supervised Relational Contrastive Learning—0
MOST: Multiple Object localization with Self-supervised Transformers for object discovery—0
Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR—0
GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph LearnerCode2
Regional Deep Atrophy: a Self-Supervised Learning Method to Automatically Identify Regions Associated With Alzheimer's Disease Progression From Longitudinal MRI—0
Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition—0
Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuitsCode0
Self-Supervised Learning of Object Segmentation from Unlabeled RGB-D Videos—0
Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide VisualizationCode2
EMP-SSL: Towards Self-Supervised Learning in One Training EpochCode2
Application of Self-Supervised Learning to MICA Model for Reconstructing Imperfect 3D Facial Structures—0
Embodied Concept Learner: Self-supervised Learning of Concepts and Mapping through Instruction Following—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