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Inception Net V1 Explained: Step-by-Step PyTorch Implementation

Learn how the Inception Net V1 architecture works and how to implement it from scratch using PyTorch. Perfect for deep learning enthusiasts wanting a hands-on understanding of this classic ...
IEEE

Fault Detection Method for Power Conversion Circuits Using Thermal Image and Convolutional Autoencoder

A fault detection method for power conversion circuits using thermal images and a convolutional autoencoder is presented. The autoencoder is trained on thermal images captured from a commercial power ...
GitHub

Paper Reproduction: Demonstrating the Superiority of QBM-VAE( VS VAE)

Replace the VAE algorithm in the paper《Design of diverse, functional mitochondrial targeting sequences across eukaryotic organisms using variational autoencoder》with the QBM-VAE algorithm, ...
IEEE

Conv4Rec: A 1-by-1 Convolutional Autoencoder for User Profiling Through Joint Analysis of Implicit and Explicit Feedback

Abstract: We introduce a new convolutional autoencoder architecture for user modeling and recommendation tasks with several improvements over the state of the art. First, our model has the flexibility ...
Scientific Research Publishing

Ghosal, I., Sarkar, S.S. and El Hallaoui, I. (2020) Lung Nodule Classification Using Convolutional Autoencoder and Clustering Augmented Learning Method (CALM). HSDM@ WSDM, 8.

TITLE: Optimizing Lung Cancer Detection in CT Imaging: A Wavelet Multi-Layer Perceptron (WMLP) Approach Enhanced by Dragonfly Algorithm (DA) ...
GitHub

autoencoder-mnist

Image Denoising with Autoencoders in R (University Project) Built a convolutional autoencoder in R using Keras/TensorFlow to perform image denoising on MNIST and CIFAR-10 datasets with varying levels ...