There are 5 repositories under convolutional-autoencoder topic.
BCDU-Net : Medical Image Segmentation
HDR image reconstruction from a single exposure using deep CNNs
This project proposes an end-to-end framework for semi-supervised Anomaly Detection and Segmentation in images based on Deep Learning.
Deep Learning sample programs using PyTorch in C++
Deep Learning-based Clustering Approaches for Bioinformatics
anomaly detection by one-class SVM
This is implementation of convolutional variational autoencoder in TensorFlow library and it will be used for video generation.
SegNet-like Autoencoders in TensorFlow
[SIGGRAPH Asia 2017] High-Quality Hyperspectral Reconstruction Using a Spectral Prior
This projects detect Anomalous Behavior through live CCTV camera feed to alert the police or local authority for faster response time. We are using Spatio Temporal AutoEncoder and more importantly three models from Keras ie; Convolutional 3D, Convolutional 2D LSTM and Convolutional 3D Transpose. 👮‍♂️👮‍♀️📹🔍🔫⚖
A convolutional autoencoder made in TFLearn.
Unsupervised deep learning system for local anomaly event detection in crowded scenes
Implementation of a convolutional auto-encoder in PyTorch
Unsupervised Image Retrieval with Convolutional Autoencoder in Tensorflow
Convolutional autoencoder for encoding/decoding RGB images in TensorFlow with high compression ratio
Use auto encoder feature extraction to facilitate classification model prediction accuracy using gradient boosting models
A convolutional auto-encoder for compressing time sequence data of stocks.
Image Compression on COCO Dataset using Convolution AutoEncoders
Cifar-10 Image Reconstruction using Auto-encoder Models
Cost function and cost gradient function for a convolutional autoencoder.
Adversarially Training of Autoencoders for Unsupervised Anomaly Segmentation
Collection of autoencoder models in Tensorflow
a convolutional autoencoder in python and keras.
Comparison of dimensionality reduction ability of different autoencoders on different datasets.
The objective is to add some noise to the images and then use an Convolutional Autoencoder to denoise them.
Time Series Forecasting using RNN, Anomaly Detection using LSTM Auto-Encoder and Compression using Convolutional Auto-Encoder
End-To-End Raw Audio Resynthesis System for Piano -> E-Piano
Convolutional Autoencoder for Denoising Images