A denoising autoencoder + adversarial losses and attention mechanisms for face swapping.

Deep count autoencoder for denoising scRNA-seq data

Implementation of the stacked denoising autoencoder in Tensorflow

[ICCV 2023 Oral] Official Implementation of "Denoising Diffusion Autoencoders are Unified Self-supervised Learners"

Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning

A simple Tensorflow based library for deep and/or denoising AutoEncoder.

Python implementation of Stacked Denoising Autoencoders for unsupervised learning of high level feature representation

Stacked denoising convolutional autoencoder written in Pytorch for some experiments.

PyTorch implementation of SDAE (Stacked Denoising AutoEncoder)

Multiple imputation utilising denoising autoencoder for approximate Bayesian inference

Finding Direction of arrival (DOA) of small UAVs using Sparse Denoising Autoencoders and Deep Neural Networks.

Stacked Denoising AutoEncoder

Code to reproduce experiments in "Accelerating Bayesian Optimization for Protein Design with Denoising Autoencoders" (Stanton et al 2022)

The hybrid model combining stacked denoising autoencoder with matrix factorization is applied, to predict the customer purchase behavior in the future month according to the purchase history and user information in the Santander dataset.

Denoising Masked Autoencoders Help Robust Classification.

Pytorch implementation of various autoencoders (contractive, denoising, convolutional, randomized)

A look at some simple autoencoders for the Cifar10 dataset, including a denoising autoencoder. Python code included.

Denoising autoencoders for anomaly detection in medical imaging

Pytorch implementation for 'Interactive Reconstruction of Monte Carlo Image Sequences using a Recurrent Denoising Autoencoder' , https://research.nvidia.com/publication/interactive-reconstruction-monte-carlo-image-sequences-using-recurrent-denoising

The repo involves a Deep Learning method, Stacked Denoising Autoencoder. The repo exploits "paracel", which is a new parallel computing framework

Tensorflow implementation for Speech Enhancement (DDAE)

Paper Detecting anomalous events in videos by learning deep representations of appearance and motion on python, opencv and tensorflow. This paper uses the stacked denoising autoencoder for the the feature training on the appearance and motion flow features as input for different window size and using multiple SVM as a single c

Medical Imaging, Denoising Autoencoder, Sparse Denoising Autoencoder (SDAE) End-to-end and Layer Wise Pretraining

A Pytorch Implementation of a denoising autoencoder.

Denoising Autoencoders for Phenotype Stratification

Denoising Adversairal Autoencoders

Image denoising using convolutional denoising autoencoders

Denoising images with a Deep Convolutional Autoencoder - Implemented in Keras

Deep Denoising Autoencoder for Seismic Random Noise Attenuation

Stacked Denoising Autoencoders (SDA) implemented in TensorFlow to analyze clinical health records and construct deep learning models to predict future patient complications.