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

Showing 701750 of 5044 papers

TitleStatusHype
Learning Anatomically Consistent Embedding for Chest RadiographyCode1
Civil Rephrases Of Toxic Texts With Self-Supervised TransformersCode1
CL4CTR: A Contrastive Learning Framework for CTR PredictionCode1
A Self-supervised Method for Entity AlignmentCode1
CLARA: Multilingual Contrastive Learning for Audio Representation AcquisitionCode1
A Fast Knowledge Distillation Framework for Visual RecognitionCode1
3D Self-Supervised Methods for Medical ImagingCode1
Distance-wise Prototypical Graph Neural Network in Node Imbalance ClassificationCode1
Distilling Visual Priors from Self-Supervised LearningCode1
Distill on the Go: Online knowledge distillation in self-supervised learningCode1
A comprehensive survey on deep active learning in medical image analysisCode1
DocMAE: Document Image Rectification via Self-supervised Representation LearningCode1
Dive into Big Model TrainingCode1
Dive into Self-Supervised Learning for Medical Image Analysis: Data, Models and TasksCode1
DEER: Descriptive Knowledge Graph for Explaining Entity RelationshipsCode1
Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal CancerCode1
Contrastive Hierarchical ClusteringCode1
High Fidelity Visualization of What Your Self-Supervised Representation Knows AboutCode1
Contrastive Graph Learning for Population-based fMRI ClassificationCode1
A foundation model for generalizable disease diagnosis in chest X-ray imagesCode1
CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIPCode1
CLIP meets DINO for Tuning Zero-Shot Classifier using Unlabeled Image CollectionsCode1
A Discrepancy Aware Framework for Robust Anomaly DetectionCode1
CLSRIL-23: Cross Lingual Speech Representations for Indic LanguagesCode1
Contrastive learning of global and local features for medical image segmentation with limited annotationsCode1
A simple, efficient and scalable contrastive masked autoencoder for learning visual representationsCode1
Hi-GMAE: Hierarchical Graph Masked AutoencodersCode1
Downstream-agnostic Adversarial ExamplesCode1
HomoGCL: Rethinking Homophily in Graph Contrastive LearningCode1
Domain Knowledge-Informed Self-Supervised Representations for Workout Form AssessmentCode1
CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image UnderstandingCode1
Physics-informed Temporal Alignment for Auto-regressive PDE Foundation ModelsCode1
CNN-based Ego-Motion Estimation for Fast MAV ManeuversCode1
CNN-JEPA: Self-Supervised Pretraining Convolutional Neural Networks Using Joint Embedding Predictive ArchitectureCode1
Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation LearningCode1
Heterogeneous Graph Masked AutoencodersCode1
CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert LinkingCode1
EXAONEPath 1.0 Patch-level Foundation Model for PathologyCode1
BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and MappingCode1
DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge ComputingCode1
Learning to Predict Navigational Patterns from Partial ObservationsCode1
COCOA: Cross Modality Contrastive Learning for Sensor DataCode1
Hierarchical Text-to-Vision Self Supervised Alignment for Improved Histopathology Representation LearningCode1
CoCoNets: Continuous Contrastive 3D Scene RepresentationsCode1
ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning ParadigmsCode1
Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical imagesCode1
Heart Failure Prediction using Modal Decomposition and Masked Autoencoders for Scarce Echocardiography DatabasesCode1
Dual Path Learning for Domain Adaptation of Semantic SegmentationCode1
Contrastive Learning of Musical RepresentationsCode1
Heterogeneous Contrastive Learning for Foundation Models and BeyondCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pretraining: NoneImages & Text57.5Unverified
2Pretraining: ShEDImages & Text54.3Unverified
3Pretraining: e-MixImages & Text48.9Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50Accuracy91.7Unverified
2ResNet18Accuracy91.02Unverified
3MV-MRAccuracy89.67Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy93.89Unverified
2ResNet18average top-1 classification accuracy92.58Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy72.51Unverified
2ResNet18average top-1 classification accuracy69.31Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy82.64Unverified
2CorInfomax (ResNet18)Top-1 Accuracy80.48Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy51.84Unverified
2ResNet18average top-1 classification accuracy51.67Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy93.18Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy71.61Unverified
#ModelMetricClaimedVerifiedStatus
1Hybrid BYOL-S/CvTAccuracy67.2Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy54.86Unverified