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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2951–3000 of 5044 papers

TitleStatusHype
Masked Momentum Contrastive Learning for Zero-shot Semantic Understanding—0
An Effective Transformer-based Contextual Model and Temporal Gate Pooling for Speaker IdentificationCode0
GOPro: Generate and Optimize Prompts in CLIP using Self-Supervised LearningCode0
Unilaterally Aggregated Contrastive Learning with Hierarchical Augmentation for Anomaly Detection—0
Quantile-based Maximum Likelihood Training for Outlier DetectionCode0
A Review on Objective-Driven Artificial Intelligence—0
Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation—0
Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation—0
Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos—0
Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning—0
Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning—0
Contrastive Learning for Lane Detection via cross-similarityCode0
The ID R&D VoxCeleb Speaker Recognition Challenge 2023 System Description—0
Self-supervised Hypergraphs for Learning Multiple World Interpretations—0
Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph—0
pNNCLR: Stochastic Pseudo Neighborhoods for Contrastive Learning based Unsupervised Representation Learning Problems—0
Advances in Self-Supervised Learning for Synthetic Aperture Sonar Data Processing, Classification, and Pattern Recognition—0
Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised LearningCode0
SSL-Auth: An Authentication Framework by Fragile Watermarking for Pre-trained Encoders in Self-supervised Learning—0
Self-supervised Learning of Rotation-invariant 3D Point Set Features using Transformer and its Self-distillationCode0
Improved Multi-Shot Diffusion-Weighted MRI with Zero-Shot Self-Supervised Learning ReconstructionCode0
A degree of image identification at sub-human scales could be possible with more advanced clusters—0
SDLFormer: A Sparse and Dense Locality-enhanced Transformer for Accelerated MR Image ReconstructionCode0
BarlowRL: Barlow Twins for Data-Efficient Reinforcement LearningCode0
Multi-Label Self-Supervised Learning with Scene Images—0
Feature-Suppressed Contrast for Self-Supervised Food Pre-trainingCode0
Exploring Visual Pre-training for Robot Manipulation: Datasets, Models and Methods—0
Class Incremental Learning with Self-Supervised Pre-Training and Prototype Learning—0
Personalization of Stress Mobile Sensing using Self-Supervised Learning—0
Federated Representation Learning for Automatic Speech Recognition—0
MAP: A Model-agnostic Pretraining Framework for Click-through Rate PredictionCode0
A Probabilistic Approach to Self-Supervised Learning using Cyclical Stochastic Gradient MCMC—0
Dynamically Scaled Temperature in Self-Supervised Contrastive LearningCode0
SALTTS: Leveraging Self-Supervised Speech Representations for improved Text-to-Speech Synthesis—0
Graph Contrastive Learning with Generative Adversarial Network—0
DINO-CXR: A self supervised method based on vision transformer for chest X-ray classification—0
Foundational Models for Fault Diagnosis of Electrical Motors—0
Can Self-Supervised Representation Learning Methods Withstand Distribution Shifts and Corruptions?Code0
Learning to Model the World with Language—0
Motion Degeneracy in Self-supervised Learning of Elevation Angle Estimation for 2D Forward-Looking Sonar—0
Mispronunciation detection using self-supervised speech representationsCode0
Self-Supervised Learning of Gait-Based Biomarkers—0
HandMIM: Pose-Aware Self-Supervised Learning for 3D Hand Mesh Estimation—0
MUSE: Multi-View Contrastive Learning for Heterophilic Graphs—0
BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection—0
AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative NormalizationCode0
Self-Supervised Learning for Improved Synthetic Aperture Sonar Target Recognition—0
Mixture of Self-Supervised LearningCode0
GaitMorph: Transforming Gait by Optimally Transporting Discrete Codes—0
Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy—0
Show:102550
← PrevPage 60 of 101Next →
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