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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3201–3250 of 5044 papers

TitleStatusHype
Maximizing Model Generalization for Machine Condition Monitoring with Self-Supervised Learning and Federated Learning—0
Retrieval-based Knowledge Augmented Vision Language Pre-training—0
Auto-Linear Phenomenon in Subsurface Imaging—0
Hopfield model with planted patterns: a teacher-student self-supervised learning model—0
Self-Supervised Multi-Object Tracking For Autonomous Driving From Consistency Across Timescales—0
Leveraging Human Feedback to Evolve and Discover Novel Emergent Behaviors in Robot Swarms—0
Objectives Matter: Understanding the Impact of Self-Supervised Objectives on Vision Transformer Representations—0
Learning imaging mechanism directly from optical microscopy observations—0
Zero-shot text-to-speech synthesis conditioned using self-supervised speech representation model—0
A Cookbook of Self-Supervised Learning—0
End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch—0
Self-supervised Learning by View Synthesis—0
Learning Symbolic Representations Through Joint GEnerative and DIscriminative Training—0
Deep Metric Learning Assisted by Intra-variance in A Semi-supervised View of Learning—0
A Revisit of the Normalized Eight-Point Algorithm and A Self-Supervised Deep Solution—0
SARF: Aliasing Relation Assisted Self-Supervised Learning for Few-shot Relation Reasoning—0
Complex Mixer for MedMNIST Classification Decathlon—0
Shuffle & Divide: Contrastive Learning for Long Text—0
To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review—0
DCELANM-Net:Medical Image Segmentation based on Dual Channel Efficient Layer Aggregation Network with Learner—0
ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision—0
Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging—0
Morph-SSL: Self-Supervision with Longitudinal Morphing to Predict AMD Progression from OCT—0
Self-Supervised Learning from Non-Object Centric Images with a Geometric Transformation Sensitive ArchitectureCode0
Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection—0
Multimodal Representation Learning of Cardiovascular Magnetic Resonance Imaging—0
A CTC Alignment-based Non-autoregressive Transformer for End-to-end Automatic Speech Recognition—0
Multi-View Graph Representation Learning Beyond HomophilyCode0
Self-supervised Auxiliary Loss for Metric Learning in Music Similarity-based Retrieval and Auto-tagging—0
Self-Supervised Scene Dynamic Recovery from Rolling Shutter Images and Events—0
Explaining, Analyzing, and Probing Representations of Self-Supervised Learning Models for Sensor-based Human Activity Recognition—0
Self-Supervised Learning based Depth Estimation from Monocular ImagesCode0
Enhancing Self-Supervised Learning for Remote Sensing with Elevation Data: A Case Study with Scarce And High Level Semantic LabelsCode0
In-Distribution and Out-of-Distribution Self-supervised ECG Representation Learning for Arrhythmia Detection—0
MOST: Multiple Object localization with Self-supervised Transformers for object discovery—0
Semi-Supervised Relational Contrastive Learning—0
Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR—0
Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph—0
A surprisingly simple technique to control the pretraining bias for better transfer: Expand or Narrow your representation—0
Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuitsCode0
Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition—0
Regional Deep Atrophy: a Self-Supervised Learning Method to Automatically Identify Regions Associated With Alzheimer's Disease Progression From Longitudinal MRI—0
Self-Supervised Learning of Object Segmentation from Unlabeled RGB-D Videos—0
Application of Self-Supervised Learning to MICA Model for Reconstructing Imperfect 3D Facial Structures—0
Rethinking Evaluation Protocols of Visual Representations Learned via Self-supervised Learning—0
Embodied Concept Learner: Self-supervised Learning of Concepts and Mapping through Instruction Following—0
Synthetic Hard Negative Samples for Contrastive Learning—0
Inductive biases in deep learning models for weather prediction—0
Localized Region Contrast for Enhancing Self-Supervised Learning in Medical Image Segmentation—0
Multi-Level Contrastive Learning for Dense Prediction TaskCode0
Show:102550
← PrevPage 65 of 101Next →
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