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 4701–4750 of 5044 papers

TitleStatusHype
Revisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentation—0
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous GraphsCode1
CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions—0
Self-Supervised Representation Learning for Detection of ACL Tear Injury in Knee MR VideosCode0
GraphCL: Contrastive Self-Supervised Learning of Graph Representations—0
Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restorationCode1
Whitening for Self-Supervised Representation LearningCode1
Data-Efficient Reinforcement Learning with Self-Predictive RepresentationsCode1
Self-Supervised Drivable Area and Road Anomaly Segmentation using RGB-D Data for Robotic WheelchairsCode0
TERA: Self-Supervised Learning of Transformer Encoder Representation for SpeechCode1
Identifying Latent Stochastic Differential EquationsCode0
Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition—0
Learning Speech Representations from Raw Audio by Joint Audiovisual Self-Supervision—0
Self-supervised Skull Reconstruction in Brain CT Images with Decompressive CraniectomyCode0
Self domain adapted networkCode1
Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal CancerCode1
FLUID: A Unified Evaluation Framework for Flexible Sequential DataCode1
Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation—0
Noise2Filter: fast, self-supervised learning and real-time reconstruction for 3D Computed Tomography—0
Self-supervised Neural Architecture Search—0
ADD: Analytically Differentiable Dynamics for Multi-Body Systems with Frictional ContactCode1
SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss NetworkCode1
A Survey on Self-supervised Pre-training for Sequential Transfer Learning in Neural Networks—0
Subject-Aware Contrastive Learning for BiosignalsCode1
Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANsCode0
Self-Supervised Learning of a Biologically-Inspired Visual Texture Model—0
Patch SVDD: Patch-level SVDD for Anomaly Detection and SegmentationCode1
Simulation of Brain Resection for Cavity Segmentation Using Self-Supervised and Semi-Supervised LearningCode0
Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks—0
​4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection—0
4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection—0
Region-of-interest guided Supervoxel Inpainting for Self-supervisionCode0
Space-Time Correspondence as a Contrastive Random WalkCode1
Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural NetworksCode1
Don’t Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights—0
The color out of space: learning self-supervised representations for Earth Observation imageryCode1
Don't Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights—0
Unsupervised Image Classification for Deep Representation LearningCode0
Embodied Self-supervised Learning by Coordinated Sampling and Training—0
Adversarial Transfer of Pose Estimation Regression—0
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech RepresentationsCode3
Transfer Learning or Self-supervised Learning? A Tale of Two Pretraining Paradigms—0
Self-Supervised Prototypical Transfer Learning for Few-Shot ClassificationCode1
Contrastive learning of global and local features for medical image segmentation with limited annotationsCode1
Self-supervised Learning on Graphs: Deep Insights and New DirectionCode1
Self-Supervised Representation Learning for Visual Anomaly Detection—0
Visual ChiralityCode1
When Does Self-Supervision Help Graph Convolutional Networks?Code1
Robot Perception enables Complex Navigation Behavior via Self-Supervised LearningCode1
ESL: Entropy-guided Self-supervised Learning for Domain Adaptation in Semantic SegmentationCode1
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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