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 2201–2250 of 5044 papers

TitleStatusHype
Deep Spectral Improvement for Unsupervised Image Instance SegmentationCode0
DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancerCode0
Benchmarking Self-Supervised Learning Methods for Accelerated MRI ReconstructionCode0
Lung Nodule-SSM: Self-Supervised Lung Nodule Detection and Classification in Thoracic CT ImagesCode0
Benchmarking Self-Supervised Contrastive Learning Methods for Image-Based Plant PhenotypingCode0
Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human ParsingCode0
Looking Beyond Corners: Contrastive Learning of Visual Representations for Keypoint Detection and Description ExtractionCode0
Deep self-supervised learning with visualisation for automatic gesture recognitionCode0
Local Masking Meets Progressive Freezing: Crafting Efficient Vision Transformers for Self-Supervised LearningCode0
Look Ahead or Look Around? A Theoretical Comparison Between Autoregressive and Masked PretrainingCode0
MAGMA: Manifold Regularization for MAEsCode0
Deep Reinforcement Learning for Synthesizing Functions in Higher-Order LogicCode0
Liver Fibrosis and NAS scoring from CT images using self-supervised learning and texture encodingCode0
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream TasksCode0
3D Face Reconstruction from A Single Image Assisted by 2D Face Images in the WildCode0
LiPCoT: Linear Predictive Coding based Tokenizer for Self-supervised Learning of Time Series Data via Language ModelsCode0
JiTTER: Jigsaw Temporal Transformer for Event Reconstruction for Self-Supervised Sound Event DetectionCode0
An Empirical Study of Accuracy-Robustness Tradeoff and Training Efficiency in Self-Supervised LearningCode0
Benchmarking Representation Learning for Natural World Image CollectionsCode0
Linear-Complexity Self-Supervised Learning for Speech ProcessingCode0
Link Prediction with Non-Contrastive LearningCode0
Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuitsCode0
LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN ArchitectureCode0
Deep Learning with Tabular Data: A Self-supervised ApproachCode0
Leveraging Pre-Trained Acoustic Feature Extractor For Affective Vocal Bursts TasksCode0
Leveraging Visual Supervision for Array-based Active Speaker Detection and LocalizationCode0
Patch-Wise Self-Supervised Visual Representation Learning: A Fine-Grained ApproachCode0
Is Limited Participant Diversity Impeding EEG-based Machine Learning?Code0
Is It a Plausible Colour? UCapsNet for Image ColourisationCode0
ISImed: A Framework for Self-Supervised Learning using Intrinsic Spatial Information in Medical ImagesCode0
Deep learning based domain adaptation for mitochondria segmentation on EM volumesCode0
Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential RecommendationCode0
Leveraging Acoustic Images for Effective Self-Supervised Audio Representation LearningCode0
Learning to Plan for Language Modeling from Unlabeled DataCode0
IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature RepresentationCode0
Benchmarking Domain Generalization Algorithms in Computational PathologyCode0
PLAD: Learning to Infer Shape Programs with Pseudo-Labels and Approximate DistributionsCode0
Learning to Reconstruct Signals From Binary MeasurementsCode0
Learning to Edit Visual Programs with Self-SupervisionCode0
HaSa: Hardness and Structure-Aware Contrastive Knowledge Graph EmbeddingCode0
Enhancing Hyperedge Prediction with Context-Aware Self-Supervised LearningCode0
Learning to Exploit Temporal Structure for Biomedical Vision-Language ProcessingCode0
Learning Useful Representations of Recurrent Neural Network Weight MatricesCode0
Leveraging image captions for selective whole slide image annotationCode0
Learning Soft Estimator of Keypoint Scale and Orientation With Probabilistic Covariant LossCode0
Deep Clustering with Diffused Sampling and Hardness-aware Self-distillationCode0
Learning Sentinel-2 reflectance dynamics for data-driven assimilation and forecastingCode0
Learning Street View Representations with Spatiotemporal ContrastCode0
DECAR: Deep Clustering for learning general-purpose Audio RepresentationsCode0
Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient EncoderCode0
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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