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 1251–1300 of 5044 papers

TitleStatusHype
SEED: Self-supervised Distillation For Visual RepresentationCode1
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy ImagesCode1
CNN-based Ego-Motion Estimation for Fast MAV ManeuversCode1
Iterative weak/self-supervised classification framework for abnormal events detectionCode1
Self-Supervised Cryo-Electron Tomography Volumetric Image Restoration From Single Noisy Volume With Sparsity ConstraintCode1
K-PLUG: KNOWLEDGE-INJECTED PRE-TRAINED LANGUAGE MODEL FOR NATURAL LANGUAGE UNDERSTANDING AND GENERATIONCode1
Co2L: Contrastive Continual LearningCode1
IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot LearningCode1
ECONET: Effective Continual Pretraining of Language Models for Event Temporal ReasoningCode1
Self-Supervised Representation Learning for Astronomical ImagesCode1
OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised LearningCode1
Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-TrainingCode1
Overcoming Language Priors with Self-supervised Learning for Visual Question AnsweringCode1
Self-Supervised Sketch-to-Image SynthesisCode1
ISD: Self-Supervised Learning by Iterative Similarity DistillationCode1
CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert LinkingCode1
HR-Depth: High Resolution Self-Supervised Monocular Depth EstimationCode1
Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling TransferCode1
Self-Supervised Hypergraph Convolutional Networks for Session-based RecommendationCode1
Context Matters: Graph-based Self-supervised Representation Learning for Medical ImagesCode1
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor NavigationCode1
Concept Generalization in Visual Representation LearningCode1
Contrastive Transformation for Self-supervised Correspondence LearningCode1
Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream TasksCode1
Super-Selfish: Self-Supervised Learning on Images with PyTorchCode1
SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological ImagesCode1
Co-mining: Self-Supervised Learning for Sparsely Annotated Object DetectionCode1
Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning​Code1
VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular DomainCode1
Bootstrap Your Own Latent - A New Approach to Self-Supervised LearningCode1
Scaling Down Deep Learning with MNIST-1DCode1
Self-Supervised Time Series Representation Learning by Inter-Intra Relational ReasoningCode1
Task Programming: Learning Data Efficient Behavior RepresentationsCode1
Self supervised contrastive learning for digital histopathologyCode1
SS-SFDA : Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous EnvironmentsCode1
How Well Do Self-Supervised Models Transfer?Code1
Dissecting Image CropsCode1
CoMatch: Semi-supervised Learning with Contrastive Graph RegularizationCode1
Boosting Contrastive Self-Supervised Learning with False Negative CancellationCode1
Node Similarity Preserving Graph Convolutional NetworksCode1
Geography-Aware Self-Supervised LearningCode1
Dense Contrastive Learning for Self-Supervised Visual Pre-TrainingCode1
Anomaly Detection in Video via Self-Supervised and Multi-Task LearningCode1
MAGNeto: An Efficient Deep Learning Method for the Extractive Tags Summarization ProblemCode1
Learning a Geometric Representation for Data-Efficient Depth Estimation via Gradient Field and Contrastive LossCode1
Learning Visual Representations for Transfer Learning by Suppressing TextureCode1
Patch2Self: Denoising Diffusion MRI with Self-Supervised LearningCode1
Understanding Pre-trained BERT for Aspect-based Sentiment AnalysisCode1
Joint Masked CPC and CTC Training for ASRCode1
Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency DetectionCode1
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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