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 1901–1950 of 5044 papers

TitleStatusHype
Large vocabulary speech recognition for languages of Africa: multilingual modeling and self-supervised learning—0
Impact Analysis of the Use of Speech and Language Models Pretrained by Self-Supersivion for Spoken Language Understanding—0
From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysis—0
IMPA-HGAE:Intra-Meta-Path Augmented Heterogeneous Graph Autoencoder—0
AU-TTT: Vision Test-Time Training model for Facial Action Unit Detection—0
Implicit 3D Human Mesh Recovery using Consistency with Pose and Shape from Unseen-view—0
From Chaos to Clarity: 3DGS in the Dark—0
Contactless Pulse Estimation Leveraging Pseudo Labels and Self-Supervision—0
CrossVideo: Self-supervised Cross-modal Contrastive Learning for Point Cloud Video Understanding—0
Improved baselines for vision-language pre-training—0
A Survey on Self-supervised Pre-training for Sequential Transfer Learning in Neural Networks—0
CONSULT: Contrastive Self-Supervised Learning for Few-shot Tumor Detection—0
Improved Intelligibility of Dysarthric Speech using Conditional Flow Matching—0
Improved Language Identification Through Cross-Lingual Self-Supervised Learning—0
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection—0
Improved Simultaneous Multi-Slice Functional MRI Using Self-supervised Deep Learning—0
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning—0
Improved Speech Pre-Training with Supervision-Enhanced Acoustic Unit—0
FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection—0
Improvements to context based self-supervised learning—0
Friends and Foes in Learning from Noisy Labels—0
Improving Accented Speech Recognition using Data Augmentation based on Unsupervised Text-to-Speech Synthesis—0
A CTC Alignment-based Non-autoregressive Transformer for End-to-end Automatic Speech Recognition—0
Improving Accent Identification and Accented Speech Recognition Under a Framework of Self-supervised Learning—0
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking—0
Large-scale Training of Foundation Models for Wearable Biosignals—0
Constrained Mean Shift for Representation Learning—0
Improving Context-Based Meta-Reinforcement Learning with Self-Supervised Trajectory Contrastive Learning—0
Frequency-Aware Self-Supervised Long-Tailed Learning—0
A survey on Self Supervised learning approaches for improving Multimodal representation learning—0
Freeze the backbones: A Parameter-Efficient Contrastive Approach to Robust Medical Vision-Language Pre-training—0
Towards domain-invariant Self-Supervised Learning with Batch Styles Standardization—0
4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection—0
CUBE360: Learning Cubic Field Representation for Monocular 360 Depth Estimation for Virtual Reality—0
Consistent 3D Hand Reconstruction in Video via self-supervised Learning—0
Foundation Models in Medical Imaging -- A Review and Outlook—0
Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning—0
CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments—0
Consistency Regularization Can Improve Robustness to Label Noise—0
A Survey on Self-supervised Contrastive Learning for Multimodal Text-Image Analysis—0
Improving label efficiency through multi-task learning on auditory data—0
Improving Lesion Segmentation in Medical Images by Global and Regional Feature Compensation—0
Large-scale Foundation Models and Generative AI for BigData Neuroscience—0
Foundation Models for ECG: Leveraging Hybrid Self-Supervised Learning for Advanced Cardiac Diagnostics—0
Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imaging—0
Foundational Models for Fault Diagnosis of Electrical Motors—0
A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond—0
Improving Object Detection with Selective Self-supervised Self-training—0
Improving out-of-distribution generalization via multi-task self-supervised pretraining—0
Self-supervised Learning for Segmentation and Quantification of Dopamine Neurons in Parkinson's Disease—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