What're the differences between PCA and autoencoder?
Oct 15, 2014 · Both PCA and autoencoder can do demension reduction, so what are the difference between them? In what situation I should use one over another?
deep learning - When should I use a variational autoencoder as …
Jan 22, 2018 · deep-learning autoencoders variational-bayes See similar questions with these tags.
Why do we need autoencoders? - Cross Validated
Mar 23, 2019 · Recently, I have been studying autoencoders. If I understood correctly, an autoencoder is a neural network where the input layer is identical to the output layer. So, the neural network tries …
What is the origin of the autoencoder neural networks?
Oct 4, 2016 · The chapter about autoencoders in Ian Goodfellow, Yoshua Bengio and Aaron Courville's Deep Learning book says: The idea of autoencoders has been part of the historical landscape of …
autoencoders - Should I be using batchnorm and/or dropout in a VAE …
May 1, 2022 · I am trying to design some generative NN models on datasets of RGB images and was debating on whether I should be using dropout and/or batch norm. Here are my thoughts (I may be …
What is the difference between convolutional neural networks ...
I can't tell you much about RBMs, but autoencoders and CNNs are two different kinds of things. An autoencoder is a neural network that is trained in an unsupervised fashion. The goal of an …
bayesian - What are variational autoencoders and to what learning …
Jan 6, 2018 · 37 As per this and this answer, autoencoders seem to be a technique that uses neural networks for dimension reduction. I would like to additionally know what is a variational autoencoder …
Why do autoencoders come under unsupervised learning?
May 18, 2021 · Because autoencoders don't match a sample to a label. Rather, they encode input distribution into common patterns (representations) along all samples and decode the …
Prior in variational autoencoders - Cross Validated
May 1, 2022 · I am currently dealing with variational autoencoders where I've read the original paper "An introduction to variational Bayes" from Kingma and Welling. I am currently still a little …
mse - Loss function for autoencoders - Cross Validated
I am experimenting a bit autoencoders, and with tensorflow I created a model that tries to reconstruct the MNIST dataset. My network is very simple: X, e1, e2, d1, Y, where e1 and e2 are encoding ...