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 3601–3650 of 5044 papers

TitleStatusHype
SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction—0
SnapshotNet: Self-supervised Feature Learning for Point Cloud Data Segmentation Using Minimal Labeled Data—0
SOAR: Self-supervision Optimized UAV Action Recognition with Efficient Object-Aware Pretraining—0
Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks—0
SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving—0
Soft Neighbors are Positive Supporters in Contrastive Visual Representation Learning—0
Solar Panel Segmentation :Self-Supervised Learning Solutions for Imperfect Datasets—0
Solutions for Fine-grained and Long-tailed Snake Species Recognition in SnakeCLEF 2022—0
Some voices are too common: Building fair speech recognition systems using the Common Voice dataset—0
SOMTP: Self-Supervised Learning-Based Optimizer for MPC-Based Safe Trajectory Planning Problems in Robotics—0
SOS! Self-supervised Learning Over Sets Of Handled Objects In Egocentric Action Recognition—0
Sounding Like a Winner? Prosodic Differences in Post-Match Interviews—0
Source-Free Domain Adaptation for Semantic Segmentation—0
SparseDet: Improving Sparsely Annotated Object Detection with Pseudo-positive Mining—0
Sparsity-Driven Parallel Imaging Consistency for Improved Self-Supervised MRI Reconstruction—0
Spatial Context-based Self-Supervised Learning for Handwritten Text Recognition—0
Spatial HuBERT: Self-supervised Spatial Speech Representation Learning for a Single Talker from Multi-channel Audio—0
Spatial Steerability of GANs via Self-Supervision from Discriminator—0
Spatio-Focal Bidirectional Disparity Estimation From a Dual-Pixel Image—0
Frequency Selective Augmentation for Video Representation Learning—0
Spatio-temporal Contrastive Domain Adaptation for Action Recognition—0
Spatio-Temporal Contrastive Self-Supervised Learning for POI-level Crowd Flow Inference—0
Spatiotemporal Field Generation Based on Hybrid Mamba-Transformer with Physics-informed Fine-tuning—0
Spatio-temporal Latent Representations for the Analysis of Acoustic Scenes in-the-wild—0
Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning—0
Spectral Informed Mamba for Robust Point Cloud Processing—0
Spectral Temporal Contrastive Learning—0
SpeechPrompt: Prompting Speech Language Models for Speech Processing Tasks—0
Speech Quality Assessment Model Based on Mixture of Experts: System-Level Performance Enhancement and Utterance-Level Challenge Analysis—0
Speech representation learning: Learning bidirectional encoders with single-view, multi-view, and multi-task methods—0
Speech Representation Learning Revisited: The Necessity of Separate Learnable Parameters and Robust Data Augmentation—0
Speech Representation Learning Through Self-supervised Pretraining And Multi-task Finetuning—0
Speech Self-Supervised Representations Benchmarking: a Case for Larger Probing Heads—0
Speech Separation based on Contrastive Learning and Deep Modularization—0
Speech separation with large-scale self-supervised learning—0
Sphere2Vec: Self-Supervised Location Representation Learning on Spherical Surfaces—0
SPICER: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation and Reconstruction—0
SPICE: Self-supervised Pitch Estimation—0
Spiral Contrastive Learning: An Efficient 3D Representation Learning Method for Unannotated CT Lesions—0
Spoofing Attacker Also Benefits from Self-Supervised Pretrained Model—0
SS-3DCapsNet: Self-supervised 3D Capsule Networks for Medical Segmentation on Less Labeled Data—0
SIAD: Self-supervised Image Anomaly Detection System—0
SSAVSV: Towards Unified Model for Self-Supervised Audio-Visual Speaker Verification—0
SSDL: Self-Supervised Dictionary Learning—0
SSDPT: Self-Supervised Dual-Path Transformer for Anomalous Sound Detection in Machine Condition Monitoring—0
SSFD: Self-Supervised Feature Distance as an MR Image Reconstruction Quality Metric—0
SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision—0
SSHR: Leveraging Self-supervised Hierarchical Representations for Multilingual Automatic Speech Recognition—0
SSL^2: Self-Supervised Learning meets Semi-Supervised Learning: Multiple Sclerosis Segmentation in 7T-MRI from large-scale 3T-MRI—0
SSL-Auth: An Authentication Framework by Fragile Watermarking for Pre-trained Encoders in Self-supervised Learning—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