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 2051–2100 of 5044 papers

TitleStatusHype
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling—0
FGR-Net:Interpretable fundus imagegradeability classification based on deepreconstruction learning—0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency—0
Conditional Augmentation for Generative Modeling—0
Few-Shot Image Classification via Contrastive Self-Supervised Learning—0
Concurrent Discrimination and Alignment for Self-Supervised Feature Learning—0
A Survey of the Self Supervised Learning Mechanisms for Vision Transformers—0
Few-Shot Classification with Contrastive Learning—0
CONCSS: Contrastive-based Context Comprehension for Dialogue-appropriate Prosody in Conversational Speech Synthesis—0
Few Edges Are Enough: Few-Shot Network Attack Detection with Graph Neural Networks—0
How Useful is Continued Pre-Training for Generative Unsupervised Domain Adaptation?—0
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
Learning Valid Dual Bounds in Constraint Programming: Boosted Lagrangian Decomposition with Self-Supervised Learning—0
FetusMapV2: Enhanced Fetal Pose Estimation in 3D Ultrasound—0
FetusMap: Fetal Pose Estimation in 3D Ultrasound—0
FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data—0
Concept Representation Learning with Contrastive Self-Supervised Learning—0
Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Images—0
FedNST: Federated Noisy Student Training for Automatic Speech Recognition—0
AggSS: An Aggregated Self-Supervised Approach for Class-Incremental Learning—0
FedMAE: Federated Self-Supervised Learning with One-Block Masked Auto-Encoder—0
Learning to Model the World with Language—0
Learning to Solve Parametric Mixed-Integer Optimal Control Problems via Differentiable Predictive Control—0
Federated Transfer Learning with Multimodal Data—0
Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency—0
Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma—0
Federated Self-supervised Speech Representations: Are We There Yet?—0
Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence—0
A Survey of Multilingual Models for Automatic Speech Recognition—0
Federated Self-Supervised Learning of Monocular Depth Estimators for Autonomous Vehicles—0
Federated Self-Supervised Learning in Heterogeneous Settings: Limits of a Baseline Approach on HAR—0
Aggregative Self-Supervised Feature Learning from a Limited Sample—0
Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation—0
A Survey of Knowledge Enhanced Pre-trained Language Models—0
Acoustic Modeling for End-to-End Empathetic Dialogue Speech Synthesis Using Linguistic and Prosodic Contexts of Dialogue History—0
Learning to Learn in a Semi-Supervised Fashion—0
Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models—0
Federated Self-supervised Learning for Heterogeneous Clients—0
Federated Self-Supervised Learning for Acoustic Event Classification—0
Complex Mixer for MedMNIST Classification Decathlon—0
Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation—0
Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis—0
Comparison of Speech Representations for the MOS Prediction System—0
A Survey of Knowledge Enhanced Pre-trained Models—0
Federated Representation Learning for Automatic Speech Recognition—0
Federated Momentum Contrastive Clustering—0
Federated Learning without Full Labels: A Survey—0
Federated Learning for Face Recognition via Intra-subject Self-supervised Learning—0
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4—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