How UNET is different from simple autoencoders? - Stack Overflow
Feb 3, 2021 · UNET architecture is like first half encoder and second half decoder . There are different variations of autoencoders like sparse , variational etc. They all compress and decompress the data But th...
neural network - What is the difference between an autoencoder …
Jun 18, 2019 · The Wikipedia page for Autoencoder, mentions, The simplest way to perform the copying task perfectly would be to duplicate the signal. Instead, autoencoders are typically forced to reconstruct the input approximately, preserving …
python - Reducing Losses of Autoencoder - Stack Overflow
May 26, 2020 · Because you are forcing the encoder to represent an information of higher dimension with an information with lower dimension. So the lower the latent dimension is, the more the autoencoder will try working toward extracting the most meaningful information out of the input, because it has limited space. So, even the loss is greater the ...
Autoencoder Gridsearch Hyperparameter tuning Keras
Apr 13, 2018 · My data shape is the same, I just generated here random numbers. In real the datas are float numbers from range -6 to 6, I scaled them as well. The Input layer size and Encoding dimension have to ...
How does binary cross entropy loss work on autoencoders?
Sep 21, 2018 · Note that in the case of input values in range [0,1] you can use binary_crossentropy, as it is usually used (e.g. Keras autoencoder tutorial and this paper). However, don't expect that the loss value becomes zero since binary_crossentropy does not return zero when both prediction and label are not either zero or one (no matter they are equal …
how to improve the accuracy of autoencoder? - Stack Overflow
Feb 12, 2019 · I have an autoencoder and I checked the accuracy of my model with different solutions like changing the number of conv layer and increase them, add or remove Batch Normalization, change the activation function, but the accuracy for all of them is similar and it does not have any improvement that is weird.
can we use autoencoders for text data - Stack Overflow
Nov 19, 2015 · You can use Autoencoder on Textual data as explained here. Autoencoder usually worked better on image data but recent approaches changed the autoencoder in a way it is also good on the text data. have a look at this. the code is also available in GitHub.
How to create autoencoder using dropout in Dense layers using …
Mar 19, 2019 · The Concept: I am trying to reconstruct the output of a numeric dataset, for which I'm trying different approaches on Autoencoders. One of the approach is to use dropout in Dense layers. The Prob...
How is a linear autoencoder equal to PCA? - Stack Overflow
Mar 5, 2017 · This paper also shows that using a linear autoencoder, it is possible not only to compute the subspace spanned by the PCA vectors, but it is actually possible to compute the principal components themselves.
Autoencoder loss is not decreasing (and starts very high)
Jul 9, 2018 · I have the following function which is supposed to autoencode my data. My data can be thought of as an image of length 100, width 2, and it has 2 channels (100, 2, 2) def construct_ae(input_shape...