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 1201–1250 of 5044 papers

TitleStatusHype
Modeling Emotions and Ethics with Large Language ModelsCode0
How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything ModelCode3
Self-Supervised Learning Featuring Small-Scale Image Dataset for Treatable Retinal Diseases Classification—0
Can We Break Free from Strong Data Augmentations in Self-Supervised Learning?Code0
An Experimental Comparison Of Multi-view Self-supervised Methods For Music TaggingCode0
DEGNN: Dual Experts Graph Neural Network Handling Both Edge and Node Feature NoiseCode0
Label-free Anomaly Detection in Aerial Agricultural Images with Masked Image Modeling—0
MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wildCode2
TSLANet: Rethinking Transformers for Time Series Representation LearningCode3
Emerging Property of Masked Token for Effective Pre-training—0
Masked Image Modeling as a Framework for Self-Supervised Learning across Eye MovementsCode0
OmniSat: Self-Supervised Modality Fusion for Earth ObservationCode2
Encoding Urban Ecologies: Automated Building Archetype Generation through Self-Supervised Learning for Energy Modeling—0
Self-Supervised Learning of Color ConstancyCode0
An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution—0
Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object ExchangeCode0
NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEGCode2
How to Craft Backdoors with Unlabeled Data Alone?Code0
LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression—0
Spiral Scanning and Self-Supervised Image Reconstruction Enable Ultra-Sparse Sampling Multispectral Photoacoustic TomographyCode0
Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision—0
Masked Modeling Duo: Towards a Universal Audio Pre-training Framework—0
scCDCG: Efficient Deep Structural Clustering for single-cell RNA-seq via Deep Cut-informed Graph EmbeddingCode1
From Barlow Twins to Triplet Training: Differentiating Dementia with Limited DataCode0
Comparing Self-Supervised Learning Techniques for Wearable Human Activity RecognitionCode1
LGSDF: Continual Global Learning of Signed Distance Fields Aided by Local UpdatingCode1
Test-Time Zero-Shot Temporal Action LocalizationCode2
Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning—0
Mixup Domain Adaptations for Dynamic Remaining Useful Life PredictionsCode1
MedIAnomaly: A comparative study of anomaly detection in medical imagesCode2
HDR Imaging for Dynamic Scenes with Events—0
A Comprehensive Survey on Self-Supervised Learning for RecommendationCode2
Multi-modal Learning for WebAssembly Reverse Engineering—0
IWNeXt: an image-wavelet domain ConvNeXt-based network for self-supervised multi-contrast MRI reconstruction—0
A Unified Membership Inference Method for Visual Self-supervised Encoder via Part-aware CapabilityCode0
Generative-Contrastive Heterogeneous Graph Neural NetworkCode0
SelfPose3d: Self-Supervised Multi-Person Multi-View 3d Pose EstimationCode2
Learning Transactions Representations for Information Management in Banks: Mastering Local, Global, and External KnowledgeCode1
Deep learning phase recovery: data-driven, physics-driven, or combining both?Code1
HypeBoy: Generative Self-Supervised Representation Learning on HypergraphsCode1
DailyMAE: Towards Pretraining Masked Autoencoders in One DayCode1
Learning to Plan for Language Modeling from Unlabeled DataCode0
Heterogeneous Contrastive Learning for Foundation Models and BeyondCode1
Exploiting Self-Supervised Constraints in Image Super-ResolutionCode1
SelfReplay: Adapting Self-Supervised Sensory Models via Adaptive Meta-Task Replay—0
3D-Speaker-Toolkit: An Open-Source Toolkit for Multimodal Speaker Verification and Diarization—0
Towards Reverse-Engineering the Brain: Brain-Derived Neuromorphic Computing Approach with Photonic, Electronic, and Ionic Dynamicity in 3D integrated circuits—0
The Bad Batches: Enhancing Self-Supervised Learning in Image Classification Through Representative Batch Curation—0
Patch Spatio-Temporal Relation Prediction for Video Anomaly Detection—0
MVEB: Self-Supervised Learning with Multi-View Entropy BottleneckCode0
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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