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 2901–2950 of 5044 papers

TitleStatusHype
Enhancing Child Vocalization Classification with Phonetically-Tuned Embeddings for Assisting Autism Diagnosis—0
Plasticity-Optimized Complementary Networks for Unsupervised Continual LearningCode0
Can large-scale vocoded spoofed data improve speech spoofing countermeasure with a self-supervised front end?—0
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning—0
ssVERDICT: Self-Supervised VERDICT-MRI for Enhanced Prostate Tumour CharacterisationCode0
Towards Federated Learning Under Resource Constraints via Layer-wise Training and Depth Dropout—0
Enhancing Hyperedge Prediction with Context-Aware Self-Supervised LearningCode0
Neural Koopman prior for data assimilationCode0
Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic Signals—0
LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech—0
Continual Robot Learning using Self-Supervised Task Inference—0
Frequency-Aware Self-Supervised Long-Tailed Learning—0
Asymmetric Clean Segments-Guided Self-Supervised Learning for Robust Speaker Verification—0
Representation Synthesis by Probabilistic Many-Valued Logic Operation in Self-Supervised Learning—0
Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction—0
ViewMix: Augmentation for Robust Representation in Self-Supervised Learning—0
Stylebook: Content-Dependent Speaking Style Modeling for Any-to-Any Voice Conversion using Only Speech Data—0
Self-Supervised Masked Digital Elevation Models Encoding for Low-Resource Downstream Tasks—0
Spatio-Temporal Contrastive Self-Supervised Learning for POI-level Crowd Flow Inference—0
Towards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missing—0
ETP: Learning Transferable ECG Representations via ECG-Text Pre-training—0
Variational Self-Supervised Contrastive Learning Using Beta Divergence—0
Non-Parametric Representation Learning with Kernels—0
PESTO: Pitch Estimation with Self-supervised Transposition-equivariant Objective—0
Robust Recommender System: A Survey and Future DirectionsCode0
Probabilistic Self-supervised Learning via Scoring Rules Minimization—0
Inferring Actual Treatment Pathways from Patient RecordsCode0
A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images—0
Acoustic-to-articulatory inversion for dysarthric speech: Are pre-trained self-supervised representations favorable?—0
Self-Supervised Video Transformers for Isolated Sign Language Recognition—0
DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate AppraisalCode0
Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction—0
Remixing-based Unsupervised Source Separation from Scratch—0
RAMP: Retrieval-Augmented MOS Prediction via Confidence-based Dynamic Weighting—0
Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation LearningCode0
Improving Small Footprint Few-shot Keyword Spotting with Supervision on Auxiliary Data—0
Unsupervised Active Learning: Optimizing Labeling Cost-Effectiveness for Automatic Speech Recognition—0
Unleash Model Potential: Bootstrapped Meta Self-supervised Learning—0
Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities—0
MS-Net: A Multi-modal Self-supervised Network for Fine-Grained Classification of Aircraft in SAR Images—0
Speech Self-Supervised Representations Benchmarking: a Case for Larger Probing Heads—0
End-to-End Driving via Self-Supervised Imitation Learning Using Camera and LiDAR Data—0
Forensic Histopathological Recognition via a Context-Aware MIL Network Powered by Self-Supervised Contrastive LearningCode0
Reinforcement Learning Based Multi-modal Feature Fusion Network for Novel Class DiscoveryCode0
Self-supervised learning for hotspot detection and isolation from thermal images—0
AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning—0
Self-supervised Learning of Implicit Shape Representation with Dense Correspondence for Deformable Objects—0
Preserving Modality Structure Improves Multi-Modal LearningCode0
Head-Tail Cooperative Learning Network for Unbiased Scene Graph GenerationCode0
MOFO: MOtion FOcused Self-Supervision for Video UnderstandingCode0
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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