Autoencoders can also be used for image denoising. Denoising is the process of removing noise from the image. A denoising encoder can be trained in an unsupervised manner. The noise can be introduced in a normal image and the autoencoder is trained against the original images. Later, the full autoencoder can be used to produce noise-free images. Nov 01, 2017 · The autoencoder is a neural network that learns to encode and decode automatically (hence, the name). So, an autoencoder can compress and decompress information. Then, can we replace the zip and… Variational Autoencoder in TensorFlow¶ The main motivation for this post was that I wanted to get more experience with both Variational Autoencoders (VAEs) and with Tensorflow . Thus, implementing the former in the latter sounded like a good idea for learning about both at the same time. These codes are TensorFlow Autoencoder implementation examples. They are inspired by very educational Keras Blog article. "Autoencoding" is a data compression algorithm where the compression and decompression functions are 1) data-specific, 2) lossy, and 3) learned automatically from examples rather than engineered by a human. Thanks. Your code is very helpful! But I have a question. Are you implementing the exact algorithm in "Auto-Encoding Variational Bayes"? Since in that paper, it use MLP to construct the encoder and decoder, which I think in the "make_encoder" function, the activation function of first layer should be tanh, but not relu.