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 2901–2950 of 5044 papers

TitleStatusHype
AI can evolve without labels: self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation—0
AI Foundation Models in Remote Sensing: A Survey—0
AIRNet: Self-Supervised Affine Registration for 3D Medical Images using Neural Networks—0
A-JEPA: Joint-Embedding Predictive Architecture Can Listen—0
A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning—0
A Learnable Self-supervised Task for Unsupervised Domain Adaptation on Point Clouds—0
Alignment and Outer Shell Isotropy for Hyperbolic Graph Contrastive Learning—0
Align Representations With Base: A New Approach to Self-Supervised Learning—0
Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples—0
A Machine Teaching Framework for Scalable Recognition—0
A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency—0
AMMU : A Survey of Transformer-based Biomedical Pretrained Language Models—0
A Model Cortical Network for Spatiotemporal Sequence Learning and Prediction—0
A Moment in the Sun: Solar Nowcasting from Multispectral Satellite Data using Self-Supervised Learning—0
A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis—0
A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation—0
A Multi-Task Foundation Model for Wireless Channel Representation Using Contrastive and Masked Autoencoder Learning—0
A Multi-view Perspective of Self-supervised Learning—0
A Mutual Information Perspective on Multiple Latent Variable Generative Models for Positive View Generation—0
An Adapter based Multi-label Pre-training for Speech Separation and Enhancement—0
An Adapter-Based Unified Model for Multiple Spoken Language Processing Tasks—0
Analysis of Augmentations for Contrastive ECG Representation Learning—0
Analysis of Self-Supervised Speech Models on Children's Speech and Infant Vocalizations—0
Analysis of Using Sigmoid Loss for Contrastive Learning—0
Analytic Study of Text-Free Speech Synthesis for Raw Audio using a Self-Supervised Learning Model—0
Analyzing Speech Unit Selection for Textless Speech-to-Speech Translation—0
Analyzing the factors affecting usefulness of Self-Supervised Pre-trained Representations for Speech Recognition—0
An Analysis of Linear Complexity Attention Substitutes with BEST-RQ—0
An ASR-free Fluency Scoring Approach with Self-Supervised Learning—0
An Asymmetric Augmented Self-Supervised Learning Method for Unsupervised Fine-Grained Image Hashing—0
An Autoencoder-based Snow Drought Index—0
AND: Audio Network Dissection for Interpreting Deep Acoustic Models—0
An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution—0
An Embedding-Dynamic Approach to Self-supervised Learning—0
An Empirical Analysis of Speech Self-Supervised Learning at Multiple Resolutions—0
An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research—0
An Empirical Study of Self-supervised Learning with Wasserstein Distance—0
An Empirical Study on Measuring the Similarity of Sentential Arguments with Language Model Domain Adaptation—0
An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis—0
A New Perspective to Boost Vision Transformer for Medical Image Classification—0
Transfer or Self-Supervised? Bridging the Performance Gap in Medical Imaging—0
An Experimental Study: Assessing the Combined Framework of WavLM and BEST-RQ for Text-to-Speech Synthesis—0
An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels—0
An Improved Self-supervised GAN via Adversarial Training—0
An Inclusive Foundation Model for Generalizable Cytogenetics in Precision Oncology—0
An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization—0
Anisotropy Is Inherent to Self-Attention in Transformers—0
Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomalies—0
Anomaly Detection by Adapting a pre-trained Vision Language Model—0
Anomaly Detection by Context Contrasting—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