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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1301–1350 of 5044 papers

TitleStatusHype
Electrocardio Panorama: Synthesizing New ECG Views with Self-supervisionCode1
Embedding in Recommender Systems: A SurveyCode1
PORTAL: Scalable Tabular Foundation Models via Content-Specific TokenizationCode1
Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum LearningCode1
Equivariant Contrastive LearningCode1
Benchmarking Detection Transfer Learning with Vision TransformersCode1
ISCL: Interdependent Self-Cooperative Learning for Unpaired Image DenoisingCode1
Deeper into Self-Supervised Monocular Indoor Depth EstimationCode1
Emerging Properties in Self-Supervised Vision TransformersCode1
ISD: Self-Supervised Learning by Iterative Similarity DistillationCode1
Adversarial Masking for Self-Supervised LearningCode1
Jointly Fine-Tuning “BERT-like” Self Supervised Models to Improve Multimodal Speech Emotion RecognitionCode1
Iterative weak/self-supervised classification framework for abnormal events detectionCode1
L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation LearningCode1
CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World DomainsCode1
Evaluating Self-Supervised Learning for Molecular Graph EmbeddingsCode1
PointOBB: Learning Oriented Object Detection via Single Point SupervisionCode1
Joint Masked CPC and CTC Training for ASRCode1
Pose-disentangled Contrastive Learning for Self-supervised Facial RepresentationCode1
eProduct: A Million-Scale Visual Search Benchmark to Address Product Recognition ChallengesCode1
Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and ReasoningCode1
Matrix Information Theory for Self-Supervised LearningCode1
A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open ChallengesCode1
Point Cloud Self-supervised Learning via 3D to Multi-view Masked AutoencoderCode1
CASS: Cross Architectural Self-Supervision for Medical Image AnalysisCode1
Empowering Collaborative Filtering with Principled Adversarial Contrastive LossCode1
KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property PredictionCode1
Geography-Aware Self-Supervised LearningCode1
K-PLUG: KNOWLEDGE-INJECTED PRE-TRAINED LANGUAGE MODEL FOR NATURAL LANGUAGE UNDERSTANDING AND GENERATIONCode1
EnCodecMAE: Leveraging neural codecs for universal audio representation learningCode1
Self-supervised Point Cloud Representation Learning via Separating Mixed ShapesCode1
Label Contrastive Coding based Graph Neural Network for Graph ClassificationCode1
COSMOS: Catching Out-of-Context Misinformation with Self-Supervised LearningCode1
Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-TrainingCode1
PointCMP: Contrastive Mask Prediction for Self-supervised Learning on Point Cloud VideosCode1
Language modeling via stochastic processesCode1
Large Pre-trained time series models for cross-domain Time series analysis tasksCode1
Large Wireless Localization Model (LWLM): A Foundation Model for Positioning in 6G NetworksCode1
Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability PerspectiveCode1
Wiener Graph Deconvolutional Network Improves Graph Self-Supervised LearningCode1
End-to-end Multi-modal Video Temporal GroundingCode1
End-to-end Multiple Instance Learning with Gradient AccumulationCode1
Energy-Based Contrastive Learning of Visual RepresentationsCode1
Lead-agnostic Self-supervised Learning for Local and Global Representations of ElectrocardiogramCode1
Learning Anatomically Consistent Embedding for Chest RadiographyCode1
Enhanced Masked Image Modeling to Avoid Model Collapse on Multi-modal MRI DatasetsCode1
CCC-wav2vec 2.0: Clustering aided Cross Contrastive Self-supervised learning of speech representationsCode1
Learning a Geometric Representation for Data-Efficient Depth Estimation via Gradient Field and Contrastive LossCode1
CCGL: Contrastive Cascade Graph LearningCode1
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid DecoderCode1
Show:102550
← PrevPage 27 of 101Next →
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