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 2601–2650 of 5044 papers

TitleStatusHype
How Useful is Continued Pre-Training for Generative Unsupervised Domain Adaptation?—0
What Do Self-Supervised Speech and Speaker Models Learn? New Findings From a Cross Model Layer-Wise Analysis—0
Detection and Recovery Against Deep Neural Network Fault Injection Attacks Based on Contrastive Learning—0
Adapting Amidst Degradation: Cross Domain Li-ion Battery Health Estimation via Physics-Guided Test-Time Training—0
Depth Anything in Medical Images: A Comparative Study—0
MLEM: Generative and Contrastive Learning as Distinct Modalities for Event SequencesCode0
MV2MAE: Multi-View Video Masked Autoencoders—0
Hybrid Transformer and Spatial-Temporal Self-Supervised Learning for Long-term Traffic Prediction—0
Deep Learning with Tabular Data: A Self-supervised ApproachCode0
Deep Clustering with Diffused Sampling and Hardness-aware Self-distillationCode0
Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation ModelsCode0
Towards Efficient and Effective Deep Clustering with Dynamic Grouping and Prototype AggregationCode0
Adaptive Crowdsourcing Via Self-Supervised Learning—0
LPNL: Scalable Link Prediction with Large Language Models—0
Learning Representations for Clustering via Partial Information Discrimination and Cross-Level InteractionCode0
FedRSU: Federated Learning for Scene Flow Estimation on Roadside UnitsCode0
Detecting and recognizing characters in Greek papyri with YOLOv8, DeiT and SimCLR—0
Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical LearningCode0
A Novel Garment Transfer Method Supervised by Distilled Knowledge of Virtual Try-on Model—0
Anisotropy Is Inherent to Self-Attention in Transformers—0
SAM-dPCR: Real-Time and High-throughput Absolute Quantification of Biological Samples Using Zero-Shot Segment Anything Model—0
LW-FedSSL: Resource-efficient Layer-wise Federated Self-supervised Learning—0
Data-driven grapheme-to-phoneme representations for a lexicon-free text-to-speech—0
Memorization in Self-Supervised Learning Improves Downstream GeneralizationCode0
POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images—0
CrossVideo: Self-supervised Cross-modal Contrastive Learning for Point Cloud Video Understanding—0
Cross-Level Multi-Instance Distillation for Self-Supervised Fine-Grained Visual Categorization—0
Revisiting Self-supervised Learning of Speech Representation from a Mutual Information Perspective—0
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?—0
Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering—0
Enhanced Few-Shot Class-Incremental Learning via Ensemble Models—0
Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discovery—0
Self-supervised Learning of Dense Hierarchical Representations for Medical Image SegmentationCode0
Learning Performance-Oriented Control Barrier Functions Under Complex Safety Constraints and Limited ActuationCode0
EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model—0
Generative Deduplication For Socia Media Data Selection—0
SwiMDiff: Scene-wide Matching Contrastive Learning with Diffusion Constraint for Remote Sensing Image—0
Noise-robust zero-shot text-to-speech synthesis conditioned on self-supervised speech-representation model with adapters—0
Siamese Networks with Soft Labels for Unsupervised Lesion Detection and Patch Pretraining on Screening Mammograms—0
TwinBooster: Synergising Large Language Models with Barlow Twins and Gradient Boosting for Enhanced Molecular Property PredictionCode0
Self-supervised Learning for Electroencephalogram: A Systematic Survey—0
Representation Learning for Wearable-Based Applications in the Case of Missing Data—0
Autosen: improving automatic wifi human sensing through cross-modal autoencoder—0
Unifying Graph Contrastive Learning via Graph Message Augmentation—0
Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection—0
Multichannel AV-wav2vec2: A Framework for Learning Multichannel Multi-Modal Speech RepresentationCode0
Enhancing Essay Scoring with Adversarial Weights Perturbation and Metric-specific AttentionPooling—0
Understanding Representation Learnability of Nonlinear Self-Supervised LearningCode0
Exploiting Data Hierarchy as a New Modality for Contrastive Learning—0
Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensing—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