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 3851–3900 of 5044 papers

TitleStatusHype
Acoustic Modeling for End-to-End Empathetic Dialogue Speech Synthesis Using Linguistic and Prosodic Contexts of Dialogue History—0
Masked Siamese ConvNets—0
Self-Supervised Implicit Attention: Guided Attention by The Model Itself—0
EDITnet: A Lightweight Network for Unsupervised Domain Adaptation in Speaker Verification—0
The ZevoMOS entry to VoiceMOS Challenge 2022—0
Test-Time Adaptation for Visual Document Understanding—0
Quantitative Imaging Principles Improves Medical Image LearningCode0
On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis—0
AuxMix: Semi-Supervised Learning with Unconstrained Unlabeled Data—0
Learning Fashion Compatibility from In-the-wild Images—0
Learning Task-Independent Game State Representations from Unlabeled Images—0
Virtual embeddings and self-consistency for self-supervised learning—0
Invariant Structure Learning for Better Generalization and Causal Explainability—0
Investigation of Ensemble features of Self-Supervised Pretrained Models for Automatic Speech Recognition—0
Transformer-based Self-Supervised Fish Segmentation in Underwater Videos—0
Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?Code0
Is Self-Supervised Learning More Robust Than Supervised Learning?—0
Federated Momentum Contrastive Clustering—0
Joint Encoder-Decoder Self-Supervised Pre-training for ASR—0
On Data Scaling in Masked Image Modeling—0
TriBYOL: Triplet BYOL for Self-Supervised Representation Learning—0
Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images—0
Layered Depth Refinement with Mask Guidance—0
Decoupled Self-supervised Learning for Non-Homophilous Graphs—0
On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning—0
Extending Momentum Contrast with Cross Similarity Consistency Regularization—0
Physics and semantic informed multi-sensor calibration via optimization theory and self-supervised learning—0
Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data—0
FedNST: Federated Noisy Student Training for Automatic Speech Recognition—0
MSR: Making Self-supervised learning Robust to Aggressive Augmentations—0
On the duality between contrastive and non-contrastive self-supervised learning—0
Toward a realistic model of speech processing in the brain with self-supervised learning—0
Self-supervised Learning of Audio Representations from Audio-Visual Data using Spatial Alignment—0
Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods—0
Understanding the Role of Nonlinearity in Training Dynamics of Contrastive Learning—0
Generalized Supervised Contrastive Learning—0
Self-supervised Learning for Label Sparsity in Computational Drug Repositioning—0
Self-Supervised Learning as a Means To Reduce the Need for Labeled Data in Medical Image AnalysisCode0
Impact Analysis of the Use of Speech and Language Models Pretrained by Self-Supersivion for Spoken Language Understanding—0
Augmentation Component Analysis: Modeling Similarity via the Augmentation OverlapsCode0
Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images—0
3D Graph Contrastive Learning for Molecular Property Prediction—0
COIN: Co-Cluster Infomax for Bipartite Graphs—0
Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark DatasetsCode0
Conformal Credal Self-Supervised LearningCode0
Analysis of Augmentations for Contrastive ECG Representation Learning—0
Is Lip Region-of-Interest Sufficient for Lipreading?—0
Data Generation for Satellite Image Classification Using Self-Supervised Representation Learning—0
Triangular Contrastive Learning on Molecular Graphs—0
Task-Customized Self-Supervised Pre-training with Scalable Dynamic Routing—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