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 1801–1850 of 5044 papers

TitleStatusHype
Knolling Bot: Learning Robotic Object Arrangement from Tidy Demonstrations—0
Fragment-based Pretraining and Finetuning on Molecular GraphsCode1
Beyond Random Augmentations: Pretraining with Hard ViewsCode0
Contextualized Structural Self-supervised Learning for Ontology MatchingCode0
Toward a Foundation Model for Time Series Data—0
Exploring DINO: Emergent Properties and Limitations for Synthetic Aperture Radar Imagery—0
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4—0
FroSSL: Frobenius Norm Minimization for Efficient Multiview Self-Supervised LearningCode0
Dual Conic Proxies for AC Optimal Power Flow—0
MIMO-NeRF: Fast Neural Rendering with Multi-input Multi-output Neural Radiance Fields—0
Understanding Masked Autoencoders From a Local Contrastive Perspective—0
MUSCLE: Multi-task Self-supervised Continual Learning to Pre-train Deep Models for X-ray Images of Multiple Body Parts—0
An Investigation of Representation and Allocation Harms in Contrastive LearningCode0
Self-supervised Learning for Anomaly Detection in Computational Workflows—0
Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised LearningCode0
uSee: Unified Speech Enhancement and Editing with Conditional Diffusion Models—0
GhostEncoder: Stealthy Backdoor Attacks with Dynamic Triggers to Pre-trained Encoders in Self-supervised Learning—0
Self-supervised Learning of Contextualized Local Visual EmbeddingsCode0
ECG-SL: Electrocardiogram(ECG) Segment Learning, a deep learning method for ECG signal—0
Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchangeCode1
SimLVSeg: Simplifying Left Ventricular Segmentation in 2D+Time Echocardiograms with Self- and Weakly-Supervised LearningCode0
PC-Adapter: Topology-Aware Adapter for Efficient Domain Adaption on Point Clouds with Rectified Pseudo-label—0
SSHR: Leveraging Self-supervised Hierarchical Representations for Multilingual Automatic Speech Recognition—0
Information Flow in Self-Supervised LearningCode1
Low-Resource Self-Supervised Learning with SSL-Enhanced TTS—0
CtxMIM: Context-Enhanced Masked Image Modeling for Remote Sensing Image Understanding—0
Multi-Granularity Click Confidence Learning via Self-Distillation in Recommendation—0
Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution SegmentationCode0
Confidence-based Visual Dispersal for Few-shot Unsupervised Domain AdaptationCode1
Towards Foundation Models Learned from Anatomy in Medical Imaging via Self-SupervisionCode1
Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Study—0
KDD-LOAM: Jointly Learned Keypoint Detector and Descriptors Assisted LiDAR Odometry and Mapping—0
The Triad of Failure Modes and a Possible Way Out—0
SEPT: Towards Efficient Scene Representation Learning for Motion Prediction—0
Joint Prediction and Denoising for Large-scale Multilingual Self-supervised Learning—0
M^33D: Learning 3D priors using Multi-Modal Masked Autoencoders for 2D image and video understanding—0
Masked Image Residual Learning for Scaling Deeper Vision TransformersCode0
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised LearningCode1
On the Impact of Quantization and Pruning of Self-Supervised Speech Models for Downstream Speech Recognition Tasks "In-the-Wild''—0
PARTICLE: Part Discovery and Contrastive Learning for Fine-grained RecognitionCode0
Fast-HuBERT: An Efficient Training Framework for Self-Supervised Speech Representation LearningCode1
Revisiting LARS for Large Batch Training Generalization of Neural Networks—0
A Study on Incorporating Whisper for Robust Speech AssessmentCode1
Understanding Calibration of Deep Neural Networks for Medical Image Classification—0
Masking Improves Contrastive Self-Supervised Learning for ConvNets, and Saliency Tells You WhereCode0
Exploring Self-supervised Skeleton-based Action Recognition in Occluded EnvironmentsCode1
A Study of Forward-Forward Algorithm for Self-Supervised Learning—0
MoDA: Leveraging Motion Priors from Videos for Advancing Unsupervised Domain Adaptation in Semantic SegmentationCode0
DimCL: Dimensional Contrastive Learning For Improving Self-Supervised Learning—0
Self-supervised learning unveils change in urban housing from street-level imagesCode0
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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