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 2251–2300 of 5044 papers

TitleStatusHype
Rethinking Data Augmentation for Tabular Data in Deep LearningCode1
Evaluation of self-supervised pre-training for automatic infant movement classification using wearable movement sensorsCode0
Adversarial Speaker Disentanglement Using Unannotated External Data for Self-supervised Representation Based Voice Conversion—0
ProtoVAE: Prototypical Networks for Unsupervised Disentanglement—0
Improved baselines for vision-language pre-training—0
Shared and Private Information Learning in Multimodal Sentiment Analysis with Deep Modal Alignment and Self-supervised Multi-Task Learning—0
GeoMAE: Masked Geometric Target Prediction for Self-supervised Point Cloud Pre-TrainingCode1
Fast Traversability Estimation for Wild Visual Navigation—0
Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations—0
How to Train Your CheXDragon: Training Chest X-Ray Models for Transfer to Novel Tasks and Healthcare Systems—0
Subject-based Non-contrastive Self-Supervised Learning for ECG Signal Processing—0
Continual Vision-Language Representation Learning with Off-Diagonal Information—0
Active Semantic Localization with Graph Neural Embedding—0
XTab: Cross-table Pretraining for Tabular TransformersCode1
Self-Supervised Learning for Point Clouds Data: A Survey—0
Comparing Foundation Models using Data Kernels—0
Exploration of Language Dependency for Japanese Self-Supervised Speech Representation Models—0
MSVQ: Self-Supervised Learning with Multiple Sample Views and QueuesCode0
Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey—0
SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understanding—0
PointCMP: Contrastive Mask Prediction for Self-supervised Learning on Point Cloud VideosCode1
Evaluating Variants of wav2vec 2.0 on Affective Vocal Burst TasksCode0
Learning Sentinel-2 reflectance dynamics for data-driven assimilation and forecastingCode0
Deep Unsupervised Learning for 3D ALS Point Cloud Change DetectionCode0
A vector quantized masked autoencoder for audiovisual speech emotion recognition—0
On the Effectiveness of Equivariant Regularization for Robust Online Continual Learning—0
Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification—0
Masked Trajectory Models for Prediction, Representation, and ControlCode1
Using Spatio-Temporal Dual-Stream Network with Self-Supervised Learning for Lung Tumor Classification on Radial Probe Endobronchial Ultrasound Video—0
Analysing the Impact of Audio Quality on the Use of Naturalistic Long-Form Recordings for Infant-Directed Speech ResearchCode0
Self-supervised learning for infant cry analysis—0
3D Molecular Geometry Analysis with 2D Graphs—0
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual RepresentationsCode1
A Simplified Framework for Contrastive Learning for Node Representations—0
Neurosymbolic AI - Why, What, and How—0
Self-supervised Activity Representation Learning with Incremental Data: An Empirical Study—0
Cross-Shaped Windows Transformer with Self-supervised Pretraining for Clinically Significant Prostate Cancer Detection in Bi-parametric MRI—0
SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification—0
Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASRCode0
AVATAR: Adversarial self-superVised domain Adaptation network for TARget domainCode0
Uncertainty-aware Self-supervised Learning for Cross-domain Technical Skill Assessment in Robot-assisted Surgery—0
Auto-Linear Phenomenon in Subsurface Imaging—0
Maximizing Model Generalization for Machine Condition Monitoring with Self-Supervised Learning and Federated Learning—0
Lightweight, Pre-trained Transformers for Remote Sensing TimeseriesCode2
Retrieval-based Knowledge Augmented Vision Language Pre-training—0
Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised LearningCode1
Hopfield model with planted patterns: a teacher-student self-supervised learning model—0
Learning to Predict Navigational Patterns from Partial ObservationsCode1
ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot LearningCode1
Leveraging Human Feedback to Evolve and Discover Novel Emergent Behaviors in Robot Swarms—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