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 3601–3650 of 5044 papers

TitleStatusHype
Failure-Proof Non-Contrastive Self-Supervised Learning—0
Fair Foundation Models for Medical Image Analysis: Challenges and Perspectives—0
Fast Adaptation for Human Pose Estimation via Meta-Optimization—0
Fast and Robust Face-to-Parameter Translation for Game Character Auto-Creation—0
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation—0
FastInject: Injecting Unpaired Text Data into CTC-based ASR training—0
Fast Traversability Estimation for Wild Visual Navigation—0
Fast Word Error Rate Estimation Using Self-Supervised Representations for Speech and Text—0
FaultSeg Swin-UNETR: Transformer-Based Self-Supervised Pretraining Model for Fault Recognition—0
FeaRLESS: Feature Refinement Loss for Ensembling Self-Supervised Learning Features in Robust End-to-end Speech Recognition—0
Feature Alignment-Based Knowledge Distillation for Efficient Compression of Large Language Models—0
Feature Alignment by Uncertainty and Self-Training for Source-Free Unsupervised Domain Adaptation—0
Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning—0
Feature diversity in self-supervised learning—0
Feature Learning in Image Hierarchies using Functional Maximal Correlation—0
Feature Normalization for Fine-tuning Self-Supervised Models in Speech Enhancement—0
Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition—0
Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging—0
Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning—0
Federated Contrastive Learning for Volumetric Medical Image Segmentation—0
Federated Contrastive Representation Learning with Feature Fusion and Neighborhood Matching—0
Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases—0
Federated Learning for Face Recognition via Intra-subject Self-supervised Learning—0
Federated Learning without Full Labels: A Survey—0
Federated Momentum Contrastive Clustering—0
Federated Representation Learning for Automatic Speech Recognition—0
Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis—0
Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation—0
Federated Self-Supervised Learning for Acoustic Event Classification—0
Federated Self-supervised Learning for Heterogeneous Clients—0
Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation—0
Federated Self-Supervised Learning in Heterogeneous Settings: Limits of a Baseline Approach on HAR—0
Federated Self-Supervised Learning of Monocular Depth Estimators for Autonomous Vehicles—0
Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence—0
Federated Self-supervised Speech Representations: Are We There Yet?—0
Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency—0
Federated Transfer Learning with Multimodal Data—0
FedMAE: Federated Self-Supervised Learning with One-Block Masked Auto-Encoder—0
FedNST: Federated Noisy Student Training for Automatic Speech Recognition—0
FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data—0
FetusMap: Fetal Pose Estimation in 3D Ultrasound—0
FetusMapV2: Enhanced Fetal Pose Estimation in 3D Ultrasound—0
How Useful is Continued Pre-Training for Generative Unsupervised Domain Adaptation?—0
Few Edges Are Enough: Few-Shot Network Attack Detection with Graph Neural Networks—0
Few-Shot Classification with Contrastive Learning—0
Few-Shot Image Classification via Contrastive Self-Supervised Learning—0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency—0
FGR-Net:Interpretable fundus imagegradeability classification based on deepreconstruction learning—0
Fill in the Gap! Combining Self-supervised Representation Learning with Neural Audio Synthesis for Speech Inpainting—0
Fine-gained air quality inference based on low-quality sensing data using self-supervised learning—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