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 3401–3450 of 5044 papers

TitleStatusHype
Augmentation-Free Graph Contrastive Learning with Performance Guarantee—0
TOV: The Original Vision Model for Optical Remote Sensing Image Understanding via Self-supervised Learning—0
SOS! Self-supervised Learning Over Sets Of Handled Objects In Egocentric Action Recognition—0
Representation Learning by Detecting Incorrect Location EmbeddingsCode0
Self-Supervised Video Representation Learning with Motion-Contrastive Perception—0
Divergence-aware Federated Self-Supervised LearningCode0
Transformer-Based Self-Supervised Learning for Emotion Recognition—0
CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data—0
Self-Supervised Graph Neural Network for Multi-Source Domain AdaptationCode1
Automatic Pronunciation Assessment using Self-Supervised Speech Representation Learning—0
Frequency Selective Augmentation for Video Representation Learning—0
MTI-Net: A Multi-Target Speech Intelligibility Prediction Model—0
Self-supervised learning for robust voice cloning—0
DDOS: A MOS Prediction Framework utilizing Domain Adaptive Pre-training and Distribution of Opinion Scores—0
MAESTRO: Matched Speech Text Representations through Modality Matching—0
Boosting Self-Supervised Embeddings for Speech EnhancementCode1
A Wav2vec2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech Recognition—0
Structure-aware Protein Self-supervised LearningCode1
Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency—0
VNIbCReg: VICReg with Neighboring-Invariance and better-Covariance Evaluated on Non-stationary Seismic Signal Time SeriesCode0
LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of Feature Similarity—0
Federated Self-supervised Speech Representations: Are We There Yet?—0
A Comparison of Deep Learning MOS Predictors for Speech Synthesis Quality—0
Repeat after me: Self-supervised learning of acoustic-to-articulatory mapping by vocal imitation—0
Self-supervised learning -- A way to minimize time and effort for precision agriculture?Code1
Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translation—0
Unsupervised Data Selection via Discrete Speech Representation for ASR—0
Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification—0
The Vicomtech Spoofing-Aware Biometric System for the SASV Challenge—0
GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution AssignmentCode1
Exemplar Learning for Medical Image Segmentation—0
Distortion-Aware Self-Supervised 360° Depth Estimation from A Single Equirectangular Projection Image—0
Mix-up Self-Supervised Learning for Contrast-agnostic Applications—0
End-to-End Integration of Speech Recognition, Speech Enhancement, and Self-Supervised Learning Representation—0
WavFT: Acoustic model finetuning with labelled and unlabelled data—0
Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function PredictionsCode2
Simplicial Embeddings in Self-Supervised Learning and Downstream ClassificationCode1
On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting—0
Selecting task with optimal transport self-supervised learning for few-shot classification—0
Analyzing the factors affecting usefulness of Self-Supervised Pre-trained Representations for Speech Recognition—0
Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification—0
SpeechPrompt: An Exploration of Prompt Tuning on Generative Spoken Language Model for Speech Processing TasksCode1
Leverage Your Local and Global Representations: A New Self-Supervised Learning StrategyCode1
How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning—0
Self-Supervised Learning for Recommender Systems: A SurveyCode2
Improving Mispronunciation Detection with Wav2vec2-based Momentum Pseudo-Labeling for Accentedness and Intelligibility AssessmentCode1
On Metric Learning for Audio-Text Cross-Modal RetrievalCode1
Self-Supervised Leaf Segmentation under Complex Lighting ConditionsCode1
SPAct: Self-supervised Privacy Preservation for Action RecognitionCode1
Self-Supervised Light Field Depth Estimation Using Epipolar Plane Images—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