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 3351–3400 of 5044 papers

TitleStatusHype
Delving Deep into Simplicity Bias for Long-Tailed Image Recognition—0
Dementia Severity Classification under Small Sample Size and Weak Supervision in Thick Slice MRI—0
Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases—0
Denoised Labels for Financial Time-Series Data via Self-Supervised Learning—0
DenseDINO: Boosting Dense Self-Supervised Learning with Token-Based Point-Level Consistency—0
Dense Self-Supervised Learning for Medical Image Segmentation—0
Deploying self-supervised learning in the wild for hybrid automatic speech recognition—0
Depth360: Self-supervised Learning for Monocular Depth Estimation using Learnable Camera Distortion Model—0
Depth Anything in Medical Images: A Comparative Study—0
Description-based Controllable Text-to-Speech with Cross-Lingual Voice Control—0
Detecting and recognizing characters in Greek papyri with YOLOv8, DeiT and SimCLR—0
Detection and Recovery Against Deep Neural Network Fault Injection Attacks Based on Contrastive Learning—0
Detection of Animal Movement from Weather Radar using Self-Supervised Learning—0
Detection of diabetic retinopathy using longitudinal self-supervised learning—0
Detection of Underwater Multi-Targets Based on Self-Supervised Learning and Deformable Path Aggregation Feature Pyramid Network—0
Developing vocal system impaired patient-aimed voice quality assessment approach using ASR representation-included multiple features—0
Diagnosis and Severity Assessment of Ulcerative Colitis using Self Supervised Learning—0
DialogueBERT: A Self-Supervised Learning based Dialogue Pre-training Encoder—0
Diffuse and Disperse: Image Generation with Representation Regularization—0
DIG: A Turnkey Library for Diving into Graph Deep Learning Research—0
Digging Into Self-Supervised Learning of Feature Descriptors—0
Digital Volumetric Biopsy Cores Improve Gleason Grading of Prostate Cancer Using Deep Learning—0
DimCL: Dimensional Contrastive Learning For Improving Self-Supervised Learning—0
DINO-CXR: A self supervised method based on vision transformer for chest X-ray classification—0
DINO-LG: A Task-Specific DINO Model for Coronary Calcium Scoring—0
DINO Pre-training for Vision-based End-to-end Autonomous Driving—0
Direct Coloring for Self-Supervised Enhanced Feature Decoupling—0
Directional Self-supervised Learning for Heavy Image Augmentations—0
Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering—0
Discriminative, Generative and Self-Supervised Approaches for Target-Agnostic Learning—0
Discriminative-Generative Representation Learning for One-Class Anomaly Detection—0
Disentangled Contrastive Learning on Graphs—0
Disentangled Generative Graph Representation Learning—0
Disentangled Speech Embeddings using Cross-modal Self-supervision—0
Disentangling the Effects of Data Augmentation and Format Transform in Self-Supervised Learning of Image Representations—0
Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events—0
Distilling Localization for Self-Supervised Representation Learning—0
DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features—0
Distortion-Aware Self-Supervised 360° Depth Estimation from A Single Equirectangular Projection Image—0
Distortion-Disentangled Contrastive Learning—0
Distributed Contrastive Learning for Medical Image Segmentation—0
Distributed Linear-Quadratic Control with Graph Neural Networks—0
Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach—0
Decentralized Unsupervised Learning of Visual Representations—0
Distribution Estimation to Automate Transformation Policies for Self-Supervision—0
Distribution Regularized Self-Supervised Learning for Domain Adaptation of Semantic Segmentation—0
Diverse Sampling for Self-Supervised Learning of Semantic Segmentation—0
Divide and Conquer Self-Supervised Learning for High-Content Imaging—0
Divide and Contrast: Self-supervised Learning from Uncurated Data—0
DMT: Comprehensive Distillation with Multiple Self-supervised Teachers—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