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 1951–2000 of 5044 papers

TitleStatusHype
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
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection—0
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning—0
FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection—0
Friends and Foes in Learning from Noisy Labels—0
A CTC Alignment-based Non-autoregressive Transformer for End-to-end Automatic Speech Recognition—0
Learning Compound Tasks without Task-specific Knowledge via Imitation and Self-supervised Learning—0
Learning Cross-view Visual Geo-localization without Ground Truth—0
Constrained Mean Shift for Representation Learning—0
Frequency-Aware Self-Supervised Long-Tailed Learning—0
Improving Small Footprint Few-shot Keyword Spotting with Supervision on Auxiliary Data—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
4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection—0
Consistent 3D Hand Reconstruction in Video via self-supervised Learning—0
Foundation Models in Medical Imaging -- A Review and Outlook—0
Improving Ultrasound Tongue Image Reconstruction from Lip Images Using Self-supervised Learning and Attention Mechanism—0
Consistency Regularization Can Improve Robustness to Label Noise—0
Improving Zero-shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions—0
A Survey on Self-supervised Contrastive Learning for Multimodal Text-Image Analysis—0
Foundation Models for ECG: Leveraging Hybrid Self-Supervised Learning for Advanced Cardiac Diagnostics—0
IMU Based Deep Stride Length Estimation With Self-Supervised Learning—0
In-Bed Human Pose Estimation from Unseen and Privacy-Preserving Image Domains—0
Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imaging—0
In-Context Symmetries: Self-Supervised Learning through Contextual World Models—0
Incorporating Attributes and Multi-Scale Structures for Heterogeneous Graph Contrastive Learning—0
Data Collection-free Masked Video Modeling—0
Foundational Models for Fault Diagnosis of Electrical Motors—0
Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis—0
Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning—0
Incremental False Negative Detection for Contrastive Learning—0
A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond—0
Incremental Layer-wise Self-Supervised Learning for Efficient Speech Domain Adaptation On Device—0
Self-supervised Learning for Segmentation and Quantification of Dopamine Neurons in Parkinson's Disease—0
In-Distribution and Out-of-Distribution Self-supervised ECG Representation Learning for Arrhythmia Detection—0
For One-Shot Decoding: Self-supervised Deep Learning-Based Polar Decoder—0
In-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classification—0
Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks—0
Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation—0
Fractal Graph Contrastive Learning—0
Balanced Deep CCA for Bird Vocalization Detection—0
Learning Complete 3D Morphable Face Models from Images and Videos—0
Learning Deep Representation with Energy-Based Self-Expressiveness for Subspace Clustering—0
Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks—0
InfoMAE: Pair-Efficient Cross-Modal Alignment for Multimodal Time-Series Sensing Signals—0
InfoNCE is variational inference in a recognition parameterised model—0
Informal Safety Guarantees for Simulated Optimizers Through Extrapolation from Partial Simulations—0
Forecasting Evolution of Clusters in Game Agents with Hebbian Learning—0
Connecting the Dots: Inferring Patent Phrase Similarity with Retrieved Phrase Graphs—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