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 2051–2100 of 5044 papers

TitleStatusHype
DSV: An Alignment Validation Loss for Self-supervised Outlier Model SelectionCode0
Self-Supervised Learning for Interactive Perception of Surgical Thread for Autonomous Suture Tail-Shortening—0
Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events—0
Mini-Batch Optimization of Contrastive LossCode1
Multimodal Molecular Pretraining via Modality Blending—0
Self-Supervised Learning with Lie Symmetries for Partial Differential EquationsCode1
On the Use of Self-Supervised Speech Representations in Spontaneous Speech Synthesis—0
On the Effectiveness of Speech Self-supervised Learning for Music—0
Advances and Challenges in Meta-Learning: A Technical Review—0
Visible and infrared self-supervised fusion trained on a single example—0
Weakly-supervised Contrastive Learning for Unsupervised Object DiscoveryCode0
Personalized Prediction of Recurrent Stress Events Using Self-Supervised Learning on Multimodal Time-Series Data—0
Encoder-Decoder Networks for Self-Supervised Pretraining and Downstream Signal Bandwidth Regression on Digital Antenna Arrays—0
Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentationCode0
In-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classification—0
Crossway Diffusion: Improving Diffusion-based Visuomotor Policy via Self-supervised LearningCode1
SelfFed: Self-supervised Federated Learning for Data Heterogeneity and Label Scarcity in IoMT—0
EmoGen: Eliminating Subjective Bias in Emotional Music Generation—0
CardiGraphormer: Unveiling the Power of Self-Supervised Learning in Revolutionizing Drug Discovery—0
The ROAD to discovery: machine learning-driven anomaly detection in radio astronomy spectrogramsCode0
Don't freeze: Finetune encoders for better Self-Supervised HAR—0
Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasksCode0
More Synergy, Less Redundancy: Exploiting Joint Mutual Information for Self-Supervised Learning—0
Implicit 3D Human Mesh Recovery using Consistency with Pose and Shape from Unseen-view—0
MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated DatasetCode2
Unsupervised 3D registration through optimization-guided cyclical self-trainingCode1
CLANet: A Comprehensive Framework for Cross-Batch Cell Line Identification Using Brightfield ImagesCode0
Semantic Positive Pairs for Enhancing Visual Representation Learning of Instance Discrimination methods—0
Multi-network Contrastive Learning Based on Global and Local Representations—0
DUET: 2D Structured and Approximately Equivariant RepresentationsCode1
A generic self-supervised learning (SSL) framework for representation learning from spectra-spatial feature of unlabeled remote sensing imagery—0
3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and Multi-Dialect Corpus for Speech Representation Disentanglement—0
Self-supervised Learning of Event-guided Video Frame Interpolation for Rolling Shutter Frames—0
Unsupervised Episode Generation for Graph Meta-learningCode1
Self-Supervised Image Captioning with CLIP—0
Learning with Difference Attention for Visually Grounded Self-supervised Representations—0
Scribble-supervised Cell Segmentation Using Multiscale Contrastive RegularizationCode0
Addressing Cold Start Problem for End-to-end Automatic Speech Scoring—0
Structuring Representation Geometry with Rotationally Equivariant Contrastive LearningCode1
How to Efficiently Adapt Large Segmentation Model(SAM) to Medical ImagesCode1
Variance-Covariance Regularization Improves Representation Learning—0
Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning—0
Manifold Contrastive Learning with Variational Lie Group OperatorsCode0
Toward Leveraging Pre-Trained Self-Supervised Frontends for Automatic Singing Voice Understanding Tasks: Three Case Studies—0
Online Self-Supervised Deep Learning for Intrusion Detection Systems—0
A Reference-less Quality Metric for Automatic Speech Recognition via Contrastive-Learning of a Multi-Language Model with Self-SupervisionCode1
Inter-Instance Similarity Modeling for Contrastive LearningCode1
End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection—0
Understanding Contrastive Learning Through the Lens of Margins—0
RedMotion: Motion Prediction via Redundancy ReductionCode1
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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