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 4251–4300 of 5044 papers

TitleStatusHype
MST: Masked Self-Supervised Transformer for Visual Representation—0
MTI-Net: A Multi-Target Speech Intelligibility Prediction Model—0
MTLoc: A Confidence-Based Source-Free Domain Adaptation Approach For Indoor Localization—0
MTSMAE: Masked Autoencoders for Multivariate Time-Series Forecasting—0
Multi-Airport Delay Prediction with Transformers—0
MultiCamCows2024 -- A Multi-view Image Dataset for AI-driven Holstein-Friesian Cattle Re-Identification on a Working Farm—0
Multi-Domain Self-Supervised Learning—0
Multi-encoder nnU-Net outperforms Transformer models with self-supervised pretraining—0
Multi-fidelity surrogate modeling for temperature field prediction using deep convolution neural network—0
Multi-Granularity Click Confidence Learning via Self-Distillation in Recommendation—0
Multi-Label Self-Supervised Learning with Scene Images—0
Multi-Level Graph Contrastive Learning—0
Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detection—0
Multilingual Speech Recognition using Knowledge Transfer across Learning Processes—0
Multi-Mask Self-Supervised Learning for Physics-Guided Neural Networks in Highly Accelerated MRI—0
UXLA: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual NLP—0
Multi-modal AI for comprehensive breast cancer prognostication—0
Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusion—0
Multimodal Deep Learning for Stroke Prediction and Detection using Retinal Imaging and Clinical Data—0
Multi-Modal Emotion Recognition by Text, Speech and Video Using Pretrained Transformers—0
Multi-modal Food Recommendation using Clustering and Self-supervised Learning—0
Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learning—0
MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures—0
Multi-modal Learning for WebAssembly Reverse Engineering—0
Multi-Modal Pre-Training for Automated Speech Recognition—0
Multimodal Representation Learning of Cardiovascular Magnetic Resonance Imaging—0
Multi-Modal Representation Learning with Text-Driven Soft Masks—0
Multimodal Self-Supervised Learning for Medical Image Analysis—0
Multimodal self-supervised learning for lesion localization—0
Multi-Modal Self-Supervised Semantic Communication—0
Multi-Modal Unsupervised Pre-Training for Surgical Operating Room Workflow Analysis—0
Multi-modal Vision Pre-training for Medical Image Analysis—0
Multi-network Contrastive Learning Based on Global and Local Representations—0
Multi-object tracking with self-supervised associating network—0
Multi-OCT-SelfNet: Integrating Self-Supervised Learning with Multi-Source Data Fusion for Enhanced Multi-Class Retinal Disease Classification—0
Multi-organ Self-supervised Contrastive Learning for Breast Lesion Segmentation—0
Multi-Parameter Molecular MRI Quantification using Physics-Informed Self-Supervised Learning—0
Multiple Object Tracking with Correlation Learning—0
Image Enhanced Rotation Prediction for Self-Supervised Learning—0
Multi-Scale Neighborhood Occupancy Masked Autoencoder for Self-Supervised Learning in LiDAR Point Clouds—0
Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation—0
Superpixel Semantics Representation and Pre-training for Vision-Language Task—0
Multi Self-supervised Pre-fine-tuned Transformer Fusion for Better Intelligent Transportation Detection—0
Multi-source Few-shot Domain Adaptation—0
Multi-Task Self-Supervised Learning for Disfluency Detection—0
Multi-task Self-Supervised Learning for Human Activity Detection—0
Multi-Task Self-Supervised Learning for Image Segmentation Task—0
Multi-Task Self-Supervised Object Detection via Recycling of Bounding Box Annotations—0
Multi-Task Self-Supervised Pre-Training for Music Classification—0
Multi-task Voice Activated Framework 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