deep learning - When should I use a variational autoencoder as …
Jan 22, 2018 · deep-learning autoencoders variational-bayes See similar questions with these tags.
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?
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 …
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 …
neural networks - 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 …
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 ...
Why binary crossentropy can be used as the loss function in …
Instead, KL-divergence is usually used as the loss function in this specific type of autoencoders. If you have any example of autoencoder trained using MSE and BCE loss and there is a noticable …
Choosing activation and loss functions in autoencoder
Jan 4, 2020 · Here is the tutorial: https://blog.keras.io/building-autoencoders-in-keras.html. However, I am confused with the choice of activation and loss for the simple one-layer autoencoder (which is the …
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 …
PCA vs linear Autoencoder: features independence
May 27, 2020 · Moreover, for linear autoencoders and contrarily to PCA, the new features we end up do not have to be independent (no orthogonality constraints in the neural networks).