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 1951–2000 of 5044 papers

TitleStatusHype
Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos—0
Masked Spatio-Temporal Structure Prediction for Self-supervised Learning on Point Cloud VideosCode1
GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space ReconstructionCode1
Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning—0
Half-Hop: A graph upsampling approach for slowing down message passingCode1
A Survey on Deep Multi-modal Learning for Body Language Recognition and GenerationCode1
Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning—0
LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series ForecastersCode1
The ID R&D VoxCeleb Speaker Recognition Challenge 2023 System Description—0
Contrastive Learning for Lane Detection via cross-similarityCode0
Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph—0
Self-supervised Hypergraphs for Learning Multiple World Interpretations—0
Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural ImagesCode1
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
Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical ImagingCode1
Generalizing Event-Based Motion Deblurring in Real-World ScenariosCode1
Fine-Grained Self-Supervised Learning with Jigsaw Puzzles for Medical Image ClassificationCode1
SSLRec: A Self-Supervised Learning Framework for RecommendationCode2
Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised LearningCode0
Dual Intents Graph Modeling for User-centric Group DiscoveryCode1
Improved Multi-Shot Diffusion-Weighted MRI with Zero-Shot Self-Supervised Learning ReconstructionCode0
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
A degree of image identification at sub-human scales could be possible with more advanced clusters—0
BarlowRL: Barlow Twins for Data-Efficient Reinforcement LearningCode0
Improving Medical Image Classification in Noisy Labels Using Only Self-supervised PretrainingCode1
Prompted Contrast with Masked Motion Modeling: Towards Versatile 3D Action Representation LearningCode1
SDLFormer: A Sparse and Dense Locality-enhanced Transformer for Accelerated MR Image ReconstructionCode0
SSL-SoilNet: A Hybrid Transformer-based Framework with Self-Supervised Learning for Large-scale Soil Organic Carbon PredictionCode1
Feature-Suppressed Contrast for Self-Supervised Food Pre-trainingCode0
Exploring Visual Pre-training for Robot Manipulation: Datasets, Models and Methods—0
Multi-Label Self-Supervised Learning with Scene Images—0
Scaling may be all you need for achieving human-level object recognition capacity with human-like visual experienceCode1
Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly DetectionCode1
A Symbolic Character-Aware Model for Solving Geometry ProblemsCode1
Personalization of Stress Mobile Sensing using Self-Supervised Learning—0
Finding Tori: Self-supervised Learning for Analyzing Korean Folk SongCode1
Class Incremental Learning with Self-Supervised Pre-Training and Prototype Learning—0
MAP: A Model-agnostic Pretraining Framework for Click-through Rate PredictionCode0
Federated Representation Learning for Automatic Speech Recognition—0
SALTTS: Leveraging Self-Supervised Speech Representations for improved Text-to-Speech Synthesis—0
A Probabilistic Approach to Self-Supervised Learning using Cyclical Stochastic Gradient MCMC—0
Dynamically Scaled Temperature in Self-Supervised Contrastive LearningCode0
Graph Contrastive Learning with Generative Adversarial Network—0
DINO-CXR: A self supervised method based on vision transformer for chest X-ray classification—0
Can Self-Supervised Representation Learning Methods Withstand Distribution Shifts and Corruptions?Code0
Learning to Model the World with Language—0
Stochastic positional embeddings improve masked image modelingCode1
CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent MaskingCode1
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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