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
DO3D: Self-supervised Learning of Decomposed Object-aware 3D Motion and Depth from Monocular Videos—0
DOA-Aware Audio-Visual Self-Supervised Learning for Sound Event Localization and Detection—0
DocAligner: Annotating Real-world Photographic Document Images by Simply Taking Pictures—0
Do Discrete Self-Supervised Representations of Speech Capture Tone Distinctions?—0
Does Visual Self-Supervision Improve Learning of Speech Representations for Emotion Recognition?—0
Domain Adapting Ability of Self-Supervised Learning for Face Recognition—0
Domain-Agnostic Clustering with Self-Distillation—0
Domain-aware Self-supervised Pre-training for Weakly-supervised Meme Analysis—0
Domain-aware Self-supervised Pre-training for Label-Efficient Meme Analysis—0
Domain Bridge for Unpaired Image-to-Image Translation and Unsupervised Domain Adaptation—0
Domain Invariant Masked Autoencoders for Self-supervised Learning from Multi-domains—0
Domain-specific optimization and diverse evaluation of self-supervised models for histopathology—0
Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature—0
Don't Be So Sure! Boosting ASR Decoding via Confidence Relaxation—0
Don't freeze: Finetune encoders for better Self-Supervised HAR—0
Don't Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights—0
Don’t Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights—0
DORec: Decomposed Object Reconstruction and Segmentation Utilizing 2D Self-Supervised Features—0
Do self-supervised speech and language models extract similar representations as human brain?—0
Rethinking Spectral Augmentation for Contrast-based Graph Self-Supervised Learning—0
Downstream Task Agnostic Speech Enhancement with Self-Supervised Representation Loss—0
DQ-Data2vec: Decoupling Quantization for Multilingual Speech Recognition—0
DQnet: Cross-Model Detail Querying for Camouflaged Object Detection—0
DRAFT: A Novel Framework to Reduce Domain Shifting in Self-supervised Learning and Its Application to Children's ASR—0
DrasCLR: A Self-supervised Framework of Learning Disease-related and Anatomy-specific Representation for 3D Medical Images—0
Dropout Regularization for Self-Supervised Learning of Transformer Encoder Speech Representation—0
DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction—0
DSPNet: Towards Slimmable Pretrained Networks based on Discriminative Self-supervised Learning—0
DTG-Net: Differentiated Teachers Guided Self-Supervised Video Action Recognition—0
Contrastive Blind Denoising Autoencoder for Real-Time Denoising of Industrial IoT Sensor Data—0
Dual-branch PolSAR Image Classification Based on GraphMAE and Local Feature Extraction—0
Dual-channel Prototype Network for few-shot Classification of Pathological Images—0
Dual Conic Proxies for AC Optimal Power Flow—0
Dual Conic Proxy for Semidefinite Relaxation of AC Optimal Power Flow—0
Dual Deep Learning Approach for Non-invasive Renal Tumour Subtyping with VERDICT-MRI—0
Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction—0
Dual Lagrangian Learning for Conic Optimization—0
DUEL: Adaptive Duplicate Elimination on Working Memory for Self-Supervised Learning—0
Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination—0
DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control—0
DynamoNet: Dynamic Action and Motion Network—0
E3D-GPT: Enhanced 3D Visual Foundation for Medical Vision-Language Model—0
EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision—0
EasyCraft: A Robust and Efficient Framework for Automatic Avatar Crafting—0
EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering—0
ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors—0
ECG-SL: Electrocardiogram(ECG) Segment Learning, a deep learning method for ECG signal—0
EchoApex: A General-Purpose Vision Foundation Model for Echocardiography—0
Echocardiogram Foundation Model -- Application 1: Estimating Ejection Fraction—0
EchoSpike Predictive Plasticity: An Online Local Learning Rule for Spiking Neural Networks—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