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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
Exploring Geometry-Aware Contrast and Clustering Harmonization for Self-Supervised 3D Object Detection—0
A Simplified Framework for Contrastive Learning for Node Representations—0
Exploring Federated Self-Supervised Learning for General Purpose Audio Understanding—0
Exploring Efficient-tuning Methods in Self-supervised Speech Models—0
CNC-Net: Self-Supervised Learning for CNC Machining Operations—0
A Simple HMM with Self-Supervised Representations for Phone Segmentation—0
Masked Image Modeling Advances 3D Medical Image Analysis—0
Clustering Properties of Self-Supervised Learning—0
Exploring Effective Mask Sampling Modeling for Neural Image Compression—0
Clustering Egocentric Images in Passive Dietary Monitoring with Self-Supervised Learning—0
Exploring Effective Distillation of Self-Supervised Speech Models for Automatic Speech Recognition—0
Exploring DINO: Emergent Properties and Limitations for Synthetic Aperture Radar Imagery—0
Exploring Balanced Feature Spaces for Representation Learning—0
Clustering-based Unsupervised Generative Relation Extraction—0
Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping—0
A contrastive-learning approach for auditory attention detection—0
Masked Generative Extractor for Synergistic Representation and 3D Generation of Point Clouds—0
Explorations in Self-Supervised Learning: Dataset Composition Testing for Object Classification—0
Exploration of Language Dependency for Japanese Self-Supervised Speech Representation Models—0
Antenna Response Consistency Driven Self-supervised Learning for WIFI-based Human Activity Recognition—0
Exploiting the potential of unlabeled endoscopic video data with self-supervised learning—0
Cluster Head Detection for Hierarchical UAV Swarm With Graph Self-supervised Learning—0
A simple baseline for domain adaptation using rotation prediction—0
Exploiting Pseudo Labels in a Self-Supervised Learning Framework for Improved Monocular Depth Estimation—0
Exploiting Motion Information from Unlabeled Videos for Static Image Action Recognition—0
Exploiting map information for self-supervised learning in motion forecasting—0
Exploiting Image Translations via Ensemble Self-Supervised Learning for Unsupervised Domain Adaptation—0
Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations: a COVID-19 case-study—0
Exploiting Data Hierarchy as a New Modality for Contrastive Learning—0
Exploiting Behavioral Consistence for Universal User Representation—0
Exploiting Audio-Visual Consistency with Partial Supervision for Spatial Audio Generation—0
CLIP2GAN: Towards Bridging Text with the Latent Space of GANs—0
LESS: Label-efficient Multi-scale Learning for Cytological Whole Slide Image Screening—0
Explicit Use of Topicality in Dialogue Response Generation—0
Explicit Mutual Information Maximization for Self-Supervised Learning—0
On the Universality of Self-Supervised Learning—0
ClimateGS: Real-Time Climate Simulation with 3D Gaussian Style Transfer—0
Explicit homography estimation improves contrastive self-supervised learning—0
Explaining, Analyzing, and Probing Representations of Self-Supervised Learning Models for Sensor-based Human Activity Recognition—0
Contrastive Representation Disentanglement for Clustering—0
Explainable Artificial Intelligence Architecture for Melanoma Diagnosis Using Indicator Localization and Self-Supervised Learning—0
CLAWS: Contrastive Learning with hard Attention and Weak Supervision—0
AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning—0
A Foundational Brain Dynamics Model via Stochastic Optimal Control—0
Masked Autoencoder for Unsupervised Video Summarization—0
ExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning—0
Exemplar Learning for Medical Image Segmentation—0
A-SFS: Semi-supervised Feature Selection based on Multi-task Self-supervision—0
Exemplar-Based Contrastive Self-Supervised Learning with Few-Shot Class Incremental Learning—0
Class Incremental Learning with Self-Supervised Pre-Training and Prototype Learning—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