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 3801–3850 of 5044 papers

TitleStatusHype
Pixel-level Correspondence for Self-Supervised Learning from Video—0
Tandem Multitask Training of Speaker Diarisation and Speech Recognition for Meeting Transcription—0
Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning—0
Vision Transformers: State of the Art and Research Challenges—0
Learning Music-Dance Representations through Explicit-Implicit Rhythm Synchronization—0
Uncertainty-Aware Self-supervised Neural Network for Liver T_1ρ Mapping with Relaxation Constraint—0
An Embedding-Dynamic Approach to Self-supervised Learning—0
Learning Invariant World State Representations with Predictive Coding—0
Aspect-Based Sentiment Analysis using Local Context Focus Mechanism with DeBERTa—0
Context-aware Self-supervised Learning for Medical Images Using Graph Neural Network—0
Histopathology DatasetGAN: Synthesizing Large-Resolution Histopathology DatasetsCode0
Image Coding for Machines with Omnipotent Feature Learning—0
Approximating Discontinuous Nash Equilibrial Values of Two-Player General-Sum Differential Games—0
Features Based Adaptive Augmentation for Graph Contrastive LearningCode0
Masked Self-Supervision for Remaining Useful Lifetime Prediction in Machine Tools—0
S^5Mars: Semi-Supervised Learning for Mars Semantic Segmentation—0
Game State Learning via Game Scene Augmentation—0
Solutions for Fine-grained and Long-tailed Snake Species Recognition in SnakeCLEF 2022—0
Intent Detection and Discovery from User Logs via Deep Semi-Supervised Contrastive Clustering—0
Explicit Use of Topicality in Dialogue Response Generation—0
How Far Can I Go ? : A Self-Supervised Approach for Deterministic Video Depth ForecastingCode0
RIT Boston at SemEval-2022 Task 5: Multimedia Misogyny Detection By Using Coherent Visual and Language Features from CLIP Model and Data-centric AI Principle—0
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images—0
TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT VolumesCode0
FeaRLESS: Feature Refinement Loss for Ensembling Self-Supervised Learning Features in Robust End-to-end Speech Recognition—0
Interventional Contrastive Learning with Meta Semantic Regularizer—0
The THUEE System Description for the IARPA OpenASR21 Challenge—0
EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering—0
Comparison of Speech Representations for the MOS Prediction System—0
Guillotine Regularization: Why removing layers is needed to improve generalization in Self-Supervised Learning—0
Wav2Vec-Aug: Improved self-supervised training with limited data—0
Self-Supervised 3D Monocular Object Detection by Recycling Bounding Boxes—0
Geometry Contrastive Learning on Heterogeneous GraphsCode0
Predicting within and across language phoneme recognition performance of self-supervised learning speech pre-trained modelsCode0
Self Supervised Learning for Few Shot Hyperspectral Image Classification—0
Self-Supervised Training with Autoencoders for Visual Anomaly Detection—0
Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion RecognitionCode0
HealNet -- Self-Supervised Acute Wound Heal-Stage Classification—0
SCIM: Simultaneous Clustering, Inference, and Mapping for Open-World Semantic Scene UnderstandingCode0
Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation LearningCode0
Supervision-Guided Codebooks for Masked Prediction in Speech Pre-training—0
TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation LearningCode0
Visualizing and Understanding Contrastive LearningCode0
Boosting Cross-Domain Speech Recognition with Self-SupervisionCode0
Distribution Regularized Self-Supervised Learning for Domain Adaptation of Semantic Segmentation—0
Self-Supervised Learning for Videos: A SurveyCode0
How Robust is Unsupervised Representation Learning to Distribution Shift?—0
Bag of Image Patch Embedding Behind the Success of Self-Supervised Learning—0
iBoot: Image-bootstrapped Self-Supervised Video Representation Learning—0
DRAFT: A Novel Framework to Reduce Domain Shifting in Self-supervised Learning and Its Application to Children's ASR—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