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 3001–3050 of 5044 papers

TitleStatusHype
How to Scale Your EMA—0
Speech representation learning: Learning bidirectional encoders with single-view, multi-view, and multi-task methods—0
MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features—0
Exploring the Integration of Speech Separation and Recognition with Self-Supervised Learning Representation—0
Self-Supervised Learning for Audio-Based Emotion Recognition—0
Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG SignalCode0
Hallucination Improves the Performance of Unsupervised Visual Representation Learning—0
Building3D: An Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Clouds—0
Language-based Action Concept Spaces Improve Video Self-Supervised Learning—0
PASTA: Pretrained Action-State Transformer Agents—0
SLPD: Slide-level Prototypical Distillation for WSIsCode0
MASR: Multi-label Aware Speech Representation—0
Revisiting Fine-Tuning Strategies for Self-supervised Medical Imaging Analysis—0
Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series—0
Self-Supervised Learning for WiFi CSI-Based Human Activity Recognition: A Systematic Study—0
Towards the Sparseness of Projection Head in Self-Supervised Learning—0
CSSL-RHA: Contrastive Self-Supervised Learning for Robust Handwriting Authentication—0
Model Adaptation for ASR in low-resource Indian Languages—0
For One-Shot Decoding: Self-supervised Deep Learning-Based Polar Decoder—0
Audio-Visual Speech Enhancement Using Self-supervised Learning to Improve Speech Intelligibility in Cochlear Implant Simulations—0
Does Double Descent Occur in Self-Supervised Learning?Code0
RegExplainer: Generating Explanations for Graph Neural Networks in Regression TasksCode0
Self-Supervised Learning for Interactive Perception of Surgical Thread for Autonomous Suture Tail-Shortening—0
Leveraging Pretrained ASR Encoders for Effective and Efficient End-to-End Speech Intent Classification and Slot Filling—0
DSV: An Alignment Validation Loss for Self-supervised Outlier Model SelectionCode0
Multi-view self-supervised learning for multivariate variable-channel time seriesCode0
Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events—0
Multimodal Molecular Pretraining via Modality Blending—0
On the Effectiveness of Speech Self-supervised Learning for Music—0
On the Use of Self-Supervised Speech Representations in Spontaneous Speech Synthesis—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
SelfFed: Self-supervised Federated Learning for Data Heterogeneity and Label Scarcity in IoMT—0
In-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classification—0
Don't freeze: Finetune encoders for better Self-Supervised HAR—0
The ROAD to discovery: machine learning-driven anomaly detection in radio astronomy spectrogramsCode0
CardiGraphormer: Unveiling the Power of Self-Supervised Learning in Revolutionizing Drug Discovery—0
EmoGen: Eliminating Subjective Bias in Emotional Music Generation—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
Semantic Positive Pairs for Enhancing Visual Representation Learning of Instance Discrimination methods—0
Multi-network Contrastive Learning Based on Global and Local Representations—0
CLANet: A Comprehensive Framework for Cross-Batch Cell Line Identification Using Brightfield ImagesCode0
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
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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