Tensorflow 2.0 implementation of Adversarial Autoencoders

Code for the paper "Topological Autoencoders" by Michael Moor, Max Horn, Bastian Rieck, and Karsten Borgwardt.

Integrated Cell project implemented in pytorch

Hands-on in-person workshop for Deep Learning with TensorFlow

Easy Reduced Basis method

Unsupervised (Self-Supervised) Clustering of Seismic Signals Using Deep Convolutional Autoencoders

The goal of this project is to solve the task of name transcription from handwriting images implementing a NN approach.

A new approach to video compression by refining the shortcomings of conventional approach and substituting each traditional component with their neural network counterpart. Our proposed work consists of motion estimation, compression and compensation and residue compression, learned end-to-end to minimize the rate-distortion trade off. The whole…

Dual CNN Models for Unsupervised Monocular Depth Estimation

RBM implemented with spiking neurons in Python. Contrastive Divergence used to train the network.

Image retrieval program made in Tensorflow supporting VGG16, VGG19, InceptionV3 and InceptionV4 pretrained networks and own trained Convolutional autoencoder.

Tensorflow Implementation of Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation (NAACL 2019).

Tensorflow implementation of adversarial auto-encoder for MNIST

Theano implementation of Sequence-to-Sequence Autoencoder

Deep learning image autoencoder that not depends on image resolution

Using Autoencoders for classification as unsupervised machine learning algorithms with Deep Learning.

Real time face landmarking using decision trees and NN autoencoders

SESF-Fuse: An Unsupervised Deep Model for Multi-Focus Image Fusion

Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders

The implementation of "Gated Attentive-Autoencoder for Content-Aware Recommendation"