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 1551–1600 of 5044 papers

TitleStatusHype
Cross-domain Self-supervised Learning for Domain Adaptation with Few Source Labels—0
AdaDim: Dimensionality Adaptation for SSL Representational Dynamics—0
2nd Place Solution for SODA10M Challenge 2021 -- Continual Detection Track—0
Robust Alzheimer's Progression Modeling using Cross-Domain Self-Supervised Deep Learning—0
Cross-domain few-shot learning with unlabelled data—0
Automatic Equalization for Individual Instrument Tracks Using Convolutional Neural Networks—0
A Mutual Information Perspective on Multiple Latent Variable Generative Models for Positive View Generation—0
ABBSPO: Adaptive Bounding Box Scaling and Symmetric Prior based Orientation Prediction for Detecting Aerial Image Objects—0
Cross-Dimensional Medical Self-Supervised Representation Learning Based on a Pseudo-3D Transformation—0
Cross-BERT for Point Cloud Pretraining—0
Automatic Detection of Out-of-body Frames in Surgical Videos for Privacy Protection Using Self-supervised Learning and Minimal Labels—0
A Multi-view Perspective of Self-supervised Learning—0
Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior—0
Identifying Terrain Physical Parameters from Vision -- Towards Physical-Parameter-Aware Locomotion and Navigation—0
Automatic Data Augmentation for Domain Adapted Fine-Tuning of Self-Supervised Speech Representations—0
Self-Supervised Tracking via Target-Aware Data Synthesis—0
AAVAE: Augmentation-Augmented Variational Autoencoders—0
Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning—0
A Multi-Task Foundation Model for Wireless Channel Representation Using Contrastive and Masked Autoencoder Learning—0
Hybrid Transformer and Spatial-Temporal Self-Supervised Learning for Long-term Traffic Prediction—0
Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning—0
A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation—0
ADAADepth: Adapting Data Augmentation and Attention for Self-Supervised Monocular Depth Estimation—0
CPT-V: A Contrastive Approach to Post-Training Quantization of Vision Transformers—0
CPS++: Improving Class-level 6D Pose and Shape Estimation From Monocular Images With Self-Supervised Learning—0
CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learning—0
A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis—0
Automated Measurement of Eczema Severity with Self-Supervised Learning—0
COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Images—0
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation—0
ACT-JEPA: Joint-Embedding Predictive Architecture Improves Policy Representation Learning—0
Unifying Self-Supervised Clustering and Energy-Based Models—0
Automated data curation for self-supervised learning in underwater acoustic analysis—0
Hybrid Deep Learning and Signal Processing for Arabic Dialect Recognition in Low-Resource Settings—0
A Moment in the Sun: Solar Nowcasting from Multispectral Satellite Data using Self-Supervised Learning—0
Hybrid Interest Modeling for Long-tailed Users—0
CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping—0
CooPre: Cooperative Pretraining for V2X Cooperative Perception—0
A Unified Model For Voice and Accent Conversion In Speech and Singing using Self-Supervised Learning and Feature Extraction—0
A Model Cortical Network for Spatiotemporal Sequence Learning and Prediction—0
Hybrid Learning: A Novel Combination of Self-Supervised and Supervised Learning for MRI Reconstruction without High-Quality Training Reference—0
ActiveSSF: An Active-Learning-Guided Self-Supervised Framework for Long-Tailed Megakaryocyte Classification—0
Convexity-based Pruning of Speech Representation Models—0
Conversational Query Rewriting with Self-supervised Learning—0
Human-Timescale Adaptation in an Open-Ended Task Space—0
Conv1D Energy-Aware Path Planner for Mobile Robots in Unstructured Environments—0
Controllable Face Manipulation and UV Map Generation by Self-supervised Learning—0
Unified Framework for Feature Extraction based on Contrastive Learning—0
AMMU : A Survey of Transformer-based Biomedical Pretrained Language Models—0
Hybrid BYOL-ViT: Efficient approach to deal with small datasets—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