TLDR: Autoencoder underfits timeseries reconstruction and just predicts average value. Question Set-up: Here is a summary of my attempt at a sequence-to-sequence autoencoder. This image was …

Sep 11, 2018 · Use this best model (manually selected by filename) and plot original image, the encoded representation made by the encoder of the autoencoder and the prediction using the …

Aug 17, 2020 · The autoencoder then works by storing inputs in terms of where they lie on the linear image of . Observe that absent the non-linear activation functions, an autoencoder essentially …

Mar 17, 2021 · Autoencoder is technically not used as a classifier in general. They learn how to encode a given image into a short vector and reconstruct the same image from the encoded vector. It is a …

Apr 15, 2020 · If you want to create an autoencoder you need to understand that you're going to reverse process after encoding. That means that if you have three convolutional layers with filters in this …

Dec 15, 2020 · Why is the decoder in an autoencoder uses a sigmoid on the last layer? Asked 5 years, 2 months ago Modified 4 years, 1 month ago Viewed 7k times

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 …

Aug 30, 2023 · Debugging autoencoder training (loss is low but reconstructed image is all black) Asked 2 years, 5 months ago Modified 2 years, 5 months ago Viewed 925 times

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, …

Nov 24, 2016 · The deep-learning autoencoder is always unsupervised learning. The "supervised" part of the article you link to is to evaluate how well it did. The following example (taken from ch.7 of my …