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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 901–950 of 5044 papers

TitleStatusHype
On Learning Discriminative Features from Synthesized Data for Self-Supervised Fine-Grained Visual Recognition—0
Mono-ViFI: A Unified Learning Framework for Self-supervised Single- and Multi-frame Monocular Depth EstimationCode2
Temporal Representation Learning for Stock Similarities and Its Applications in Investment Management—0
On the Discriminability of Self-Supervised Representation Learning—0
Linear-Complexity Self-Supervised Learning for Speech ProcessingCode0
Universal Facial Encoding of Codec Avatars from VR Headsets—0
SafePowerGraph: Safety-aware Evaluation of Graph Neural Networks for Transmission Power GridsCode0
On Diversity in Discriminative Neural Networks—0
Label-Efficient 3D Brain Segmentation via Complementary 2D Diffusion Models with Orthogonal Views—0
GraphGuard: Contrastive Self-Supervised Learning for Credit-Card Fraud Detection in Multi-Relational Dynamic Graphs—0
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream TasksCode0
Siamese Transformer Networks for Few-shot Image Classification—0
CroMo-Mixup: Augmenting Cross-Model Representations for Continual Self-Supervised LearningCode0
Relational Representation DistillationCode1
A Closer Look at Benchmarking Self-Supervised Pre-training with Image Classification—0
An efficient framework based on large foundation model for cervical cytopathology whole slide image screeningCode0
Discriminative and Consistent Representation DistillationCode1
Universal Sound Separation with Self-Supervised Audio Masked Autoencoder—0
Efficient Unsupervised Visual Representation Learning with Explicit Cluster BalancingCode0
DINO Pre-training for Vision-based End-to-end Autonomous Driving—0
Enhanced Masked Image Modeling to Avoid Model Collapse on Multi-modal MRI DatasetsCode1
Joint-Embedding Predictive Architecture for Self-Supervised Learning of Mask Classification Architecture—0
A Self-Supervised Learning Pipeline for Demographically Fair Facial Attribute Classification—0
Shape2Scene: 3D Scene Representation Learning Through Pre-training on Shape DataCode0
On the Role of Discrete Tokenization in Visual Representation LearningCode0
Tissue-Contrastive Semi-Masked Autoencoders for Segmentation Pretraining on Chest CT—0
stEnTrans: Transformer-based deep learning for spatial transcriptomics enhancementCode1
Bootstrapping Vision-language Models for Self-supervised Remote Physiological Measurement—0
SCPNet: Unsupervised Cross-modal Homography Estimation via Intra-modal Self-supervised LearningCode1
Pan-cancer Histopathology WSI Pre-training with Position-aware Masked AutoencoderCode1
TIP: Tabular-Image Pre-training for Multimodal Classification with Incomplete DataCode2
Benchmarking Embedding Aggregation Methods in Computational Pathology: A Clinical Data PerspectiveCode1
AnatoMask: Enhancing Medical Image Segmentation with Reconstruction-guided Self-maskingCode1
Robust and Explainable Framework to Address Data Scarcity in Diagnostic Imaging—0
A Clinical Benchmark of Public Self-Supervised Pathology Foundation ModelsCode0
TE-SSL: Time and Event-aware Self Supervised Learning for Alzheimer's Disease Progression AnalysisCode0
Self-supervised visual learning from interactions with objectsCode0
Analyzing Speech Unit Selection for Textless Speech-to-Speech Translation—0
Leveraging image captions for selective whole slide image annotationCode0
LaFAM: Unsupervised Feature Attribution with Label-free Activation MapsCode0
Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised LearningCode0
Test-time adaptation for geospatial point cloud semantic segmentation with distinct domain shifts—0
Transfer or Self-Supervised? Bridging the Performance Gap in Medical Imaging—0
MSP-Podcast SER Challenge 2024: L'antenne du Ventoux Multimodal Self-Supervised Learning for Speech Emotion Recognition—0
Self-supervised Learning via Cluster Distance Prediction for Operating Room Context Awareness—0
Performance Analysis of Speech Encoders for Low-Resource SLU and ASR in Tunisian Dialect—0
Multi-modal Masked Siamese Network Improves Chest X-Ray Representation LearningCode0
Learning to (Learn at Test Time): RNNs with Expressive Hidden StatesCode5
Improving Accented Speech Recognition using Data Augmentation based on Unsupervised Text-to-Speech Synthesis—0
Improving Self-supervised Pre-training using Accent-Specific CodebooksCode1
Show:102550
← PrevPage 19 of 101Next →
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