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 4901–4950 of 5044 papers

TitleStatusHype
AfriHuBERT: A self-supervised speech representation model for African languagesCode0
Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-MixingCode0
AFiRe: Anatomy-Driven Self-Supervised Learning for Fine-Grained Representation in Radiographic ImagesCode0
AtmoDist: Self-supervised Representation Learning for Atmospheric DynamicsCode0
A Simple Framework Uniting Visual In-context Learning with Masked Image Modeling to Improve Ultrasound SegmentationCode0
Adversarial Skill Networks: Unsupervised Robot Skill Learning from VideoCode0
Task-Agnostic Graph Neural Network Evaluation via Adversarial CollaborationCode0
Self-Supervised Neural Architecture Search for Imbalanced DatasetsCode0
A Self-supervised Learning System for Object Detection in Videos Using Random Walks on GraphsCode0
Cross-domain Transfer of Valence Preferences via a Meta-optimization ApproachCode0
Trip-ROMA: Self-Supervised Learning with Triplets and Random MappingsCode0
ActBERT: Learning Global-Local Video-Text RepresentationsCode0
Improving Self-Supervised Learning-based MOS Prediction NetworksCode0
Self-supervised OCT Image Denoising with Slice-to-Slice Registration and ReconstructionCode0
Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region AlignmentCode0
TRUSformer: Improving Prostate Cancer Detection from Micro-Ultrasound Using Attention and Self-SupervisionCode0
Improving Image Clustering through Sample Ranking and Its Application to remote--sensing imagesCode0
Self-Supervised Physics-Based Deep Learning MRI Reconstruction Without Fully-Sampled DataCode0
Task-Free Continual LearningCode0
Improving Fine-tuning of Self-supervised Models with Contrastive InitializationCode0
Improving Children's Speech Recognition by Fine-tuning Self-supervised Adult Speech RepresentationsCode0
Improving Acoustic Word Embeddings through Correspondence Training of Self-supervised Speech RepresentationsCode0
Improved transferability of self-supervised learning models through batch normalization finetuningCode0
A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose EstimationCode0
Improved Multi-Shot Diffusion-Weighted MRI with Zero-Shot Self-Supervised Learning ReconstructionCode0
Improved acoustic-to-articulatory inversion using representations from pretrained self-supervised learning modelsCode0
Adversarial Self-Supervised Learning for Out-of-Domain DetectionCode0
IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue ClassificationCode0
Cross-domain Contrastive Learning for Unsupervised Domain AdaptationCode0
Cross and Learn: Cross-Modal Self-SupervisionCode0
TRUSWorthy: Toward Clinically Applicable Deep Learning for Confident Detection of Prostate Cancer in Micro-UltrasoundCode0
Identify Then Recommend: Towards Unsupervised Group RecommendationCode0
Self-supervised pseudo-colorizing of masked cellsCode0
IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image SegmentationCode0
CroMo-Mixup: Augmenting Cross-Model Representations for Continual Self-Supervised LearningCode0
Dialogue-adaptive Language Model Pre-training From Quality EstimationCode0
Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI dataCode0
Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer's DiseaseCode0
Motifs-based Recommender System via Hypergraph Convolution and Contrastive LearningCode0
Credal Self-Supervised LearningCode0
COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised LearningCode0
Boosting Few-Shot Visual Learning with Self-SupervisionCode0
CoSeg: Cognitively Inspired Unsupervised Generic Event SegmentationCode0
Hypergraph Self-supervised Learning with Sampling-efficient SignalsCode0
Boosting Cross-Domain Speech Recognition with Self-SupervisionCode0
Self-Supervised Representation Learning by Rotation Feature DecouplingCode0
Cooperative Knowledge Distillation: A Learner Agnostic ApproachCode0
TEE4EHR: Transformer Event Encoder for Better Representation Learning in Electronic Health RecordsCode0
BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised LearningCode0
Self-Supervised Representation Learning for Detection of ACL Tear Injury in Knee MR VideosCode0
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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