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 4601–4650 of 5044 papers

TitleStatusHype
Speech Analysis of Language Varieties in ItalyCode0
Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised LearningCode0
Dynamically Scaled Temperature in Self-Supervised Contrastive LearningCode0
Circumventing Backdoor Space via Weight SymmetryCode0
An Effective Transformer-based Contextual Model and Temporal Gate Pooling for Speaker IdentificationCode0
Dynamic Entity-Masked Graph Diffusion Model for histopathological image Representation LearningCode0
Self-Supervised Knowledge Assimilation for Expert-Layman Text Style TransferCode0
Masked Image Residual Learning for Scaling Deeper Vision TransformersCode0
A Dual-Task Synergy-Driven Generalization Framework for Pancreatic Cancer Segmentation in CT ScansCode0
Towards Label-Efficient Incremental Learning: A SurveyCode0
Circuit Design and Efficient Simulation of Quantum Inner Product and Empirical Studies of Its Effect on Near-Term Hybrid Quantum-Classic Machine LearningCode0
Speech Self-Supervised Representation Benchmarking: Are We Doing it Right?Code0
Self-Supervised Learning as a Means To Reduce the Need for Labeled Data in Medical Image AnalysisCode0
Masked Image Modelling for retinal OCT understandingCode0
Choice of training label matters: how to best use deep learning for quantitative MRI parameter estimationCode0
Towards Lifelong Self-Supervision For Unpaired Image-to-Image TranslationCode0
Self-Supervised Learning based Depth Estimation from Monocular ImagesCode0
Self-Supervised Learning Based Handwriting VerificationCode0
Balancing Graph Embedding Smoothness in Self-Supervised Learning via Information-Theoretic DecompositionCode0
Self-Supervised Learning Based on Spatial Awareness for Medical Image AnalysisCode0
Dynamic Channel Selection in Self-Supervised LearningCode0
Towards Model-Based Data Acquisition for Subjective Multi-Task NLP ProblemsCode0
Spherical Tree-Sliced Wasserstein DistanceCode0
Self-Supervised Learning by Cross-Modal Audio-Video ClusteringCode0
Morphing Tokens Draw Strong Masked Image ModelsCode0
Cervical Optical Coherence Tomography Image Classification Based on Contrastive Self-Supervised Texture LearningCode0
Masked Image Modeling Boosting Semi-Supervised Semantic SegmentationCode0
CERT: Contrastive Self-supervised Learning for Language UnderstandingCode0
DyG2Vec: Efficient Representation Learning for Dynamic GraphsCode0
DualAug: Exploiting Additional Heavy Augmentation with OOD Data RejectionCode0
AV-DTEC: Self-Supervised Audio-Visual Fusion for Drone Trajectory Estimation and ClassificationCode0
Self-supervised learning for analysis of temporal and morphological drug effects in cancer cell imaging dataCode0
Masked Image Modeling as a Framework for Self-Supervised Learning across Eye MovementsCode0
Masked Autoencoders are PDE LearnersCode0
Spiral Scanning and Self-Supervised Image Reconstruction Enable Ultra-Sparse Sampling Multispectral Photoacoustic TomographyCode0
An attention-based backend allowing efficient fine-tuning of transformer models for speaker verificationCode0
DSV: An Alignment Validation Loss for Self-supervised Outlier Model SelectionCode0
DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoVCode0
Unsqueeze [CLS] Bottleneck to Learn Rich RepresentationsCode0
MAP: A Model-agnostic Pretraining Framework for Click-through Rate PredictionCode0
AVATAR: Adversarial self-superVised domain Adaptation network for TARget domainCode0
Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark DatasetsCode0
SSAD: Self-supervised Auxiliary Detection Framework for Panoramic X-ray based Dental Disease DiagnosisCode0
Self-supervised learning for classifying paranasal anomalies in the maxillary sinusCode0
Self-supervised Learning for Clustering of Wireless Spectrum ActivityCode0
Self-Supervised Learning for Color Spike Camera ReconstructionCode0
Self-Supervised Learning for Contextualized Extractive SummarizationCode0
Drawing the Line: Deep Segmentation for Extracting Art from Ancient Etruscan MirrorsCode0
Ablation study of self-supervised learning for image classificationCode0
Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasksCode0
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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