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 4201–4250 of 5044 papers

TitleStatusHype
MEmoBERT: Pre-training Model with Prompt-based Learning for Multimodal Emotion Recognition—0
MeSa: Masked, Geometric, and Supervised Pre-training for Monocular Depth Estimation—0
MetaComp: Learning to Adapt for Online Depth Completion—0
Metadata-Enhanced Speech Emotion Recognition: Augmented Residual Integration and Co-Attention in Two-Stage Fine-Tuning—0
MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images—0
Meta-Representational Predictive Coding: Biomimetic Self-Supervised Learning—0
Meta Transfer of Self-Supervised Knowledge: Foundation Model in Action for Post-Traumatic Epilepsy Prediction—0
Meta-TTT: A Meta-learning Minimax Framework For Test-Time Training—0
Migrate Demographic Group For Fair GNNs—0
MimCo: Masked Image Modeling Pre-training with Contrastive Teacher—0
MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysis—0
MIMO-NeRF: Fast Neural Rendering with Multi-input Multi-output Neural Radiance Fields—0
MIMRS: A Survey on Masked Image Modeling in Remote Sensing—0
MindSemantix: Deciphering Brain Visual Experiences with a Brain-Language Model—0
Minimalistic Unsupervised Learning with the Sparse Manifold Transform—0
Mining the Explainability and Generalization: Fact Verification Based on Self-Instruction—0
MIRAM: Masked Image Reconstruction Across Multiple Scales for Breast Lesion Risk Prediction—0
Misinformation Detection in Social Media Video Posts—0
MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction—0
Mitigating Instance-Dependent Label Noise: Integrating Self-Supervised Pretraining with Pseudo-Label Refinement—0
Mitigating Overfitting in Medical Imaging: Self-Supervised Pretraining vs. ImageNet Transfer Learning for Dermatological Diagnosis—0
Mixtraining: A Better Trade-Off Between Compute and Performance—0
Mixture of Experts in a Mixture of RL settings—0
Mix-up Self-Supervised Learning for Contrast-agnostic Applications—0
ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets—0
ML-SUPERB: Multilingual Speech Universal PERformance Benchmark—0
MLVICX: Multi-Level Variance-Covariance Exploration for Chest X-ray Self-Supervised Representation Learning—0
Mobility-Induced Graph Learning for WiFi Positioning—0
MoCA: Multi-modal Cross-masked Autoencoder for Digital Health Measurements—0
ModalTune: Fine-Tuning Slide-Level Foundation Models with Multi-Modal Information for Multi-task Learning in Digital Pathology—0
Model Adaptation for ASR in low-resource Indian Languages—0
Model Extraction Attack against Self-supervised Speech Models—0
Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning—0
MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning—0
MOMA:Distill from Self-Supervised Teachers—0
Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation—0
Monocular Depth Estimation with Self-supervised Instance Adaptation—0
More Speaking or More Speakers?—0
More Synergy, Less Redundancy: Exploiting Joint Mutual Information for Self-Supervised Learning—0
Morph-SSL: Self-Supervision with Longitudinal Morphing to Predict AMD Progression from OCT—0
MOST: Multiple Object localization with Self-supervised Transformers for object discovery—0
Motif-Driven Contrastive Learning of Graph Representations—0
Motion Degeneracy in Self-supervised Learning of Elevation Angle Estimation for 2D Forward-Looking Sonar—0
Motion Forecasting for Autonomous Vehicles: A Survey—0
MPE4G: Multimodal Pretrained Encoder for Co-Speech Gesture Generation—0
MPS-AMS: Masked Patches Selection and Adaptive Masking Strategy Based Self-Supervised Medical Image Segmentation—0
MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR—0
MS-Net: A Multi-modal Self-supervised Network for Fine-Grained Classification of Aircraft in SAR Images—0
MSP-Podcast SER Challenge 2024: L'antenne du Ventoux Multimodal Self-Supervised Learning for Speech Emotion Recognition—0
MSR: Making Self-supervised learning Robust to Aggressive Augmentations—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