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 2001–2050 of 5044 papers

TitleStatusHype
Information-guided pixel augmentation for pixel-wise contrastive learning—0
Foundation Models in Medical Imaging -- A Review and Outlook—0
DCELANM-Net:Medical Image Segmentation based on Dual Channel Efficient Layer Aggregation Network with Learner—0
Consistency Regularization Can Improve Robustness to Label Noise—0
Informed Mixing -- Improving Open Set Recognition via Attribution-based Augmentation—0
Infusing Linguistic Knowledge of SMILES into Chemical Language Models—0
A Survey on Self-supervised Contrastive Learning for Multimodal Text-Image Analysis—0
InsCon:Instance Consistency Feature Representation via Self-Supervised Learning—0
Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understanding—0
Insect-Foundation: A Foundation Model and Large Multimodal Dataset for Vision-Language Insect Understanding—0
Learning neural audio features without supervision—0
Instance and Category Supervision are Alternate Learners for Continual Learning—0
Instance-aware Self-supervised Learning for Nuclei Segmentation—0
Instance Image Retrieval by Learning Purely From Within the Dataset—0
Foundation Models for ECG: Leveraging Hybrid Self-Supervised Learning for Advanced Cardiac Diagnostics—0
Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imaging—0
Foundational Models for Fault Diagnosis of Electrical Motors—0
Integration of Self-Supervised BYOL in Semi-Supervised Medical Image Recognition—0
A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond—0
Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation—0
Self-supervised Learning for Segmentation and Quantification of Dopamine Neurons in Parkinson's Disease—0
Intent Detection and Discovery from User Logs via Deep Semi-Supervised Contrastive Clustering—0
For One-Shot Decoding: Self-supervised Deep Learning-Based Polar Decoder—0
Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks—0
Fractal Graph Contrastive Learning—0
Interest-oriented Universal User Representation via Contrastive Learning—0
Learning Object-Centric Video Models by Contrasting Sets—0
LEARNING PHONEME-LEVEL DISCRETE SPEECH REPRESENTATION WITH WORD-LEVEL SUPERVISION—0
Learning Symbolic Representations Through Joint GEnerative and DIscriminative Training—0
Forecasting Evolution of Clusters in Game Agents with Hebbian Learning—0
Connecting the Dots: Inferring Patent Phrase Similarity with Retrieved Phrase Graphs—0
Interpretable agent communication from scratch (with a generic visual processor emerging on the side)—0
Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph—0
Interpretable Feature Interaction via Statistical Self-supervised Learning on Tabular Data—0
Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy—0
Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning—0
Interpretable Self-supervised Multi-task Learning for COVID-19 Information Retrieval and Extraction—0
Interrogating Paradigms in Self-supervised Graph Representation Learning—0
Learning Mask Invariant Mutual Information for Masked Image Modeling—0
FloorplanMAE:A self-supervised framework for complete floorplan generation from partial inputs—0
Flaky Performances when Pretraining on Relational Databases—0
Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis—0
Learning Low-Rank Feature for Thorax Disease Classification—0
Deep Active Ensemble Sampling For Image Classification—0
Deep Active Learning Using Barlow Twins—0
Learning Minimal Representations with Model Invariance—0
FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization—0
Conformer-Based Self-Supervised Learning for Non-Speech Audio Tasks—0
A Cross Branch Fusion-Based Contrastive Learning Framework for Point Cloud Self-supervised Learning—0
FisheyeDistanceNet: Self-Supervised Scale-Aware Distance Estimation using Monocular Fisheye Camera for Autonomous Driving—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