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Methods/Deep Belief Network

Deep Belief Network

Computer VisionIntroduced 200971 papers

Description

A Deep Belief Network (DBN) is a multi-layer generative graphical model. DBNs have bi-directional connections (RBM-type connections) on the top layer while the bottom layers only have top-down connections. They are trained using layerwise pre-training. Pre-training occurs by training the network component by component bottom up: treating the first two layers as an RBM and training, then treating the second layer and third layer as another RBM and training for those parameters.

Source: Origins of Deep Learning

Image Source: Wikipedia

Papers Using This Method

A Novel Approach using CapsNet and Deep Belief Network for Detection and Identification of Oral Leukopenia2025-01-01Neural Network Certification Informed Power System Transient Stability Preventive Control with Renewable Energy2024-11-13Deep Learning-Based Prediction of Suspension Dynamics Performance in Multi-Axle Vehicles2024-10-03Abstraction requires breadth: a renormalisation group approach2024-07-01Understanding Deep Neural Networks via Linear Separability of Hidden Layers2023-07-26From Actions to Events: A Transfer Learning Approach Using Improved Deep Belief Networks2022-11-30A Novel Approach for Neuromorphic Vision Data Compression based on Deep Belief Network2022-10-27A Novel Light Field Coding Scheme Based on Deep Belief Network & Weighted Binary Images for Additive Layered Displays2022-10-04Explainable Deep Belief Network based Auto encoder using novel Extended Garson Algorithm2022-07-18Deep Learning-Based Device-Free Localization in Wireless Sensor Networks2022-06-16Wind power ramp prediction algorithm based on wavelet deep belief network2022-02-11GM Score: Incorporating inter-class and intra-class generator diversity, discriminability of disentangled representation, and sample fidelity for evaluating GANs2021-12-13A Distillation Learning Model of Adaptive Structural Deep Belief Network for AffectNet: Facial Expression Image Database2021-10-25An Adaptive Structural Learning of Deep Belief Network for Image-based Crack Detection in Concrete Structures Using SDNET20182021-10-25Automatic Extraction of Road Networks from Satellite Images by using Adaptive Structural Deep Belief Network2021-10-25An Embedded System for Image-based Crack Detection by using Fine-Tuning model of Adaptive Structural Learning of Deep Belief Network2021-10-25Restricted Boltzmann Machine and Deep Belief Network: Tutorial and Survey2021-07-26Image Segmentation, Compression and Reconstruction from Edge Distribution Estimation with Random Field and Random Cluster Theories2021-04-09DigitalExposome: Quantifying the Urban Environment Influence on Wellbeing based on Real-Time Multi-Sensor Fusion and Deep Belief Network2021-01-29A Layer-Wise Information Reinforcement Approach to Improve Learning in Deep Belief Networks2021-01-17