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 1701–1750 of 5044 papers

TitleStatusHype
In-Situ Melt Pool Characterization via Thermal Imaging for Defect Detection in Directed Energy Deposition Using Vision Transformers—0
An Adapter Based Pre-Training for Efficient and Scalable Self-Supervised Speech Representation Learning—0
Continual Vision-Language Representation Learning with Off-Diagonal Information—0
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning—0
Generative Adapter: Contextualizing Language Models in Parameters with A Single Forward Pass—0
A-JEPA: Joint-Embedding Predictive Architecture Can Listen—0
Generating Music Medleys via Playing Music Puzzle Games—0
Go-tuning: Improving Zero-shot Learning Abilities of Smaller Language Models—0
General-Purpose Speech Representation Learning through a Self-Supervised Multi-Granularity Framework—0
Grafit: Learning fine-grained image representations with coarse labels—0
Continual Self-Supervised Learning with Masked Autoencoders in Remote Sensing—0
Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification—0
Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning—0
Graph Adversarial Self-Supervised Learning—0
INoD: Injected Noise Discriminator for Self-Supervised Representation Learning in Agricultural Fields—0
Continual Self-supervised Learning Considering Medical Domain Knowledge in Chest CT Images—0
Graph Anomaly Detection via Adaptive Test-time Representation Learning across Out-of-Distribution Domains—0
Continual Robot Learning using Self-Supervised Task Inference—0
Atmospheric Turbulence Removal with Video Sequence Deep Visual Priors—0
Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks—0
Active Foundational Models for Fault Diagnosis of Electrical Motors—0
Generalizable Re-Identification from Videos with Cycle Association—0
Generalizable Pancreas Segmentation via a Dual Self-Supervised Learning Framework—0
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning—0
Generalised Co-Salient Object Detection—0
Graph Contrastive Learning with Generative Adversarial Network—0
AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning—0
AIRNet: Self-Supervised Affine Registration for 3D Medical Images using Neural Networks—0
Infusing Linguistic Knowledge of SMILES into Chemical Language Models—0
InsCon:Instance Consistency Feature Representation via Self-Supervised Learning—0
Instance and Category Supervision are Alternate Learners for Continual Learning—0
Intent Detection and Discovery from User Logs via Deep Semi-Supervised Contrastive Clustering—0
GenDistiller: Distilling Pre-trained Language Models based on an Autoregressive Generative Model—0
GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition—0
X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning—0
Graph Neural Network-Based Distributed Optimal Control for Linear Networked Systems: An Online Distributed Training Approach—0
GelFlow: Self-supervised Learning of Optical Flow for Vision-Based Tactile Sensor Displacement Measurement—0
Graph Neural Networks for Molecules—0
GEDI: GEnerative and DIscriminative Training for Self-Supervised Learning—0
Graph Neural Networks: Methods, Applications, and Opportunities—0
Graph Positional Autoencoders as Self-supervised Learners—0
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization—0
AI Foundation Models in Remote Sensing: A Survey—0
Action Spotting and Precise Event Detection in Sports: Datasets, Methods, and Challenges—0
Self-supervised learning of hologram reconstruction using physics consistency—0
Continual Contrastive Finetuning Improves Low-Resource Relation Extraction—0
Gaussian Masked Autoencoders—0
Graph Soft-Contrastive Learning via Neighborhood Ranking—0
Gaussian2Scene: 3D Scene Representation Learning via Self-supervised Learning with 3D Gaussian Splatting—0
Continual Barlow Twins: continual self-supervised learning for remote sensing semantic segmentation—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