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 3201–3250 of 5044 papers

TitleStatusHype
MatchGAN: A Self-Supervised Semi-Supervised Conditional Generative Adversarial Network—0
Matching Multiple Perspectives for Efficient Representation Learning—0
Match to Win: Analysing Sequences Lengths for Efficient Self-supervised Learning in Speech and Audio—0
Maximizing Model Generalization for Machine Condition Monitoring with Self-Supervised Learning and Federated Learning—0
Maximum Manifold Capacity Representations in State Representation Learning—0
M-BEST-RQ: A Multi-Channel Speech Foundation Model for Smart Glasses—0
MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals—0
MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features—0
MCP: Self-supervised Pre-training for Personalized Chatbots with Multi-level Contrastive Sampling—0
MCR-Data2vec 2.0: Improving Self-supervised Speech Pre-training via Model-level Consistency Regularization—0
MC-SSL0.0: Towards Multi-Concept Self-Supervised Learning—0
MDReg-Net: Multi-resolution diffeomorphic image registration using fully convolutional networks with deep self-supervision—0
Measuring the Effectiveness of Self-Supervised Learning using Calibrated Learning Curves—0
Medical Transformer: Universal Brain Encoder for 3D MRI Analysis—0
Medical Vision Language Pretraining: A survey—0
Medical visual question answering using joint self-supervised learning—0
MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training in Radiology—0
Membership Inference Attack Against Masked Image Modeling—0
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
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?—0
Self-supervised clarification question generation for ambiguous multi-turn conversation—0
Self-supervised classification of dynamic obstacles using the temporal information provided by videos—0
Self-supervised Cloth Reconstruction via Action-conditioned Cloth Tracking—0
Self-Supervised Contextual Bandits in Computer Vision—0
Self-Supervised Contextual Language Representation of Radiology Reports to Improve the Identification of Communication Urgency—0
Self-Supervised Continuous Control without Policy Gradient—0
Self-supervised Contrastive Learning for Cross-domain Hyperspectral Image Representation—0
Self-Supervised Contrastive Pre-Training for Multivariate Point Processes—0
Self-Supervised Contrastive Representation Learning for 3D Mesh Segmentation—0
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution—0
Self supervised convolutional kernel based handcrafted feature harmonization: Enhanced left ventricle hypertension disease phenotyping on echocardiography—0
Self-supervised cost of transport estimation for multimodal path planning—0
Self-Supervised Curricular Deep Learning for Chest X-Ray Image Classification—0
Curriculum learning for self-supervised speaker verification—0
Self-supervised debiasing using low rank regularization—0
Self-Supervised Deep Learning to Enhance Breast Cancer Detection on Screening Mammography—0
Self-supervised Dense 3D Reconstruction from Monocular Endoscopic Video—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