Questions tagged [autoencoder]

Autoencoders are a type of neural network that learns a useful encoding for data in an unsupervised manner.

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input of Auto-Encoder as a feature extraction for training is similar to data that we use later for a classification model?

I have a data set of images, for example, 200 images, I want to use Autoencoder as a feature compressor. I use for example 150 for train the autoencoder and 50 for evaluation. after train and evaluate ...
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Multioutput prediction using LSTM encoder decoder with Attention

(I am working on Jupter notebook with python version 3.6.12, running Tensorflow 2.4.0 version.) I have a dataset that consists of 5 input features and 3 output features (that requires to be predicted)....
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Is the explained variance a good metric for autoencoders?

I want to evaluate how an autoencoder will perform on my data. Now, I can do this with the mean squared error of the decoded data compared to the original data, and this is fine when comparing this ...
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Correct approach to scale (min-max scaler) both input and output signal data for unsupervised learning?

I am working on a denoising autoencoder problem with noisy and clean signals. Before I pass the signals to my model I want to apply min-max normalization and am unsure of the correct way to apply this....
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Understanding forward process in diffusion models

I was reading a blog on diffusion models where I came across this expression. I didn't understand why it is \begin{align} \sqrt[]{1-\beta \small{t}}*\large{x}\small{t-1} \end{align} and what exactly ...
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Converting lists of categorical data to numeric vectors with unlabeled data

I am preparing some data for an autoencoder. One of the variables, diag_codes, is a list of codes associated with each observation. They are of varying lengths but have at least one. My question is, ...
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selection of loss function to avoid overfitting by autoencoder in prediction a figure with a sharp rise

I have to select the loss function to avoid overfitting by autoencoder in prediction of this figure that has a sharp raise, I would like to find how to avoid overfitting by autoencoder in prediction a ...
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Deep autoencoder: validation loss doesn't change

I'm trying to understand autoencoders and reproduced some code from Keras documentation: ...
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28 views

Word embedding autoencoder

I'm trying to train a word embedding autoencoder, but it either doesn't train, or trains but doesn't make predictions. I know I'm doing something wrong, so any help is greatly appreciated. Here is my ...
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Trouble with anomaly/novelty detection (on microscale) - need easy practical guide with Keras

I am relatively new to the field of machine learning. However, I already have solved simple image classification tasks with Keras (for example building CNNs and classifying MNIST...). The rough deep ...
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How to improve my deep LSTM model for time series?

I want to train a deep model for my time series power consumption dataset. I have created a model consist of CNN, BILSTM, Encoder-Decoder, and dense layers. here is my model: ...
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Generating unique points with an auto-encoder

I have been working on some research using a type of auto-encoder to generate new points with specific desirable properties. I trained my network and successfully generated some points, but when I ...
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Autoencoder in keras and accuracy

I am looking at Autoencoders in keras. They say, "Autoencoding" is a data compression algorithm where the compression and decompression functions are 1) data-specific, 2) lossy, and 3) ...
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Generate full face from half face image in Tensorflow

Has anyone found any examples of how an ML model can generate an image of a complete face using only an image of half a face? I figured this would be a case I'd be able to find easily on Google but I ...
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GAN that generates locally accurate results

I'm wondering if there are any resources for problems of the following nature: Instead of training a GAN on a bunch of different faces, train a GAN on a very small number of faces (even 1), and ...
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Having difficulty reconstructing output for one-hot encoded vectors from an Autoencoder

I am building an Autoencoder to reconstruct strings which I one-hot encoded. The dimensions of each data point in one-hot encoding is (1000,65). This is the body of the network: ...
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VAE generating same results during test time

I have trained a VAE to generate a style transferred sentence, from a negative sentence to a positive sentence. The underlying concept of VAE tells us that the sampling is done randomly, to which Mean ...
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LSTM Autoencoder for variable length data

I have a dataset composed of data about client interactions. For each client I have a variable number of interactions ( client A has 2 interactions, client B has 1 interaction, etc...) This ...
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Custom keras callbacks and changing weight (beta) of regularization term in variational autoencoder loss function

The variational autoencoder loss function is this: Loss = Loss_reconstruction + Beta * Loss_kld. I am trying to efficiently implement Kullback-Liebler Divergence Cyclic Annealing--that is changing the ...
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Autoencoder not learning walk forward image transformation

I have a series of 15 frames with (60 rows x 50 columns). Over the course of those 15 frames, the moon moves from the top left to the bottom right. Data = https://github.com/aiqc/AIQC/tree/main/...
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69 views

time series anomaly detection

I want to ask for time series anomaly detection we can apply tnn on multiple features or not? I used transformer for sentiment analysis where I have to provide a sentence and it predicts its output as ...
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23 views

LSTM decoder with 2d's input

I am developing a CNN-LSTM autoencoder in pytorch to predict time sequences. The CNN input is a RGB image: RGB image => tensor[Batch size= 4, channel = 3,width= 256, height=256] and the output is ...
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55 views

Conditional variational autoencoder: Feeding labeled MNIST to encoder with Keras

I am looking for a code implementation of a CVAE using MNIST in Keras. I found this Youtube video: https://youtu.be/8wrLjnQ7EWQ that does VAE, but I am not sure how do I convert this and make encoder ...
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How do I prevent infinite variances/standard deviations in my variational autoencoder?

I am working on a project with a variational autoencoder (VAE). The problem I have is that the encoder part of VAE is producing large log variances, which leads to even larger standard deviations, ...
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single layer autoencoder performing a lot worse than pca

I am trying to use a single layer autoencoder with linear activation function to perform dimensionality reduction on a dataset before clustering. The data consists of 5000 samples with 2000 features ...
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If the input to the autoencoder is normalized, do we need to use sigmoid on the last layer?

According to: https://stackoverflow.com/questions/65307833/why-is-the-decoder-in-an-autoencoder-uses-a-sigmoid-on-the-last-layer The last layer activation function contains sigmoid in order to the ...
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51 views

Explainability and Autoencoders

suppose I have an autoencoder as a two-stack LSTM that takes in sequences of $n$ features of some length $m$. Let's say that the dimension of my encoding vector is $k$, so the architecture is of the ...
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Why autoencoders use binary_crossentropy loss and not mean squared error?

Keras autoencoders examples: (https://blog.keras.io/building-autoencoders-in-keras.html) use binary_crossentropy (BCE) as loss function. Why they use ...
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How to interpreter Binary Cross Entropy loss function?

I saw some examples of Autoencoders (on images) which use sigmoid as output layer and BinaryCrossentropy as loss function. The ...
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32 views

Convolutional autoencoder - why keras example is asymmetry model?

I'm looking on keras convolutional autoencoder example, and confused with the model structure: ...
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23 views

How to walk forward an LSTM autoencoder by n timesteps?

I am able to fit this autoencoder to my sequence in order to reconstruct it. However, how would I be able to walk this forward 3 timesteps to get [[11.0], [12.0], [13.0]]? ...
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120 views

Autoencoder: Size of out_backprop doesn't match computed

This question was asked before and non of the answered worked for, I have the code ...
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1answer
37 views

Anomaly detection using LSTM AutoEncoder

Having a sequence of 10 days of sensors events, and a true / false label, specifying if the sensor triggered an alert within the 10 days duration: sensor_id timestamp feature_1 feature_2 ...
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Anomaly Detection: LSTM Autoencoder Zero Reconstruction Loss on Anomalies

I am using an LSTM Autoencoder model for time series anomaly detection. None of the anomalies get flagged because the reconstruction loss comes out to be zero for all data points on the clearly ...
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How to derive Evidence Lower Bound in the paper "Zero-Shot Text-to-Image Generation"?

Can someone share the derivation of Evidence Lower Bound in this paper ? Zero-Shot Text-to-Image Generation The overall procedure can be viewed as maximizing the evidence lower bound (ELB) (Kingma &...
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Can we use only one autoencoder for removing noise and converting a greyscale image to color image?

I am new to deep learning. out of curiosity, I have doubt about autoencoders. I want to construct a greyscale image by removing noise in it and converting it into a colour image. We can do this by ...
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Autoencoder: good loss values while fitting, but the actual performance is bad

I'm implementing a 1D convolutional autoencoder to reduce the dimensionality of an array. Here is my architecture: ...
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30 views

GAN model with different optimization functions

Building GAN model contains the following steps: Build generator model, and choose ...
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1answer
27 views

Autoencoder give wrong results

I have studied Autoencoders and tried to implement a simple one. I have built a model with one hidden layer. I ran it with the MNIST digits dataset and plotted the digits before the Autoencoder and ...
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44 views

Latent variable graph in Variational Autoencoder

I followed this Keras documentation guide about Auto Encoders. At the end of the documentation there is the graph of the latent variable z: But I can not understand and how to interpret the plot, ...
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24 views

Autoencoder train and test accuracy shooting to 99% on few epochs

I am trying to train an autoencoder for dimensionality reduction and hopefully for anomaly detection. My data specifications are as follows. Unlabeled 1 million data points 9 features I am trying to ...
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Trouble understanding the odd behavior of a trained Autoencoder

I have built an Autoencoder model (aim is to do anomaly detection). Due to large difference in scale of different features, I have transformed the data into [0,1] interval using ...
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VAE will always results in somewhat different latent vectors for same input?

Hey I was wondering if my intuition is correct that for the same input in a VAE we will get a slightly different vector every time we feed it through the network, due to the random sampling operation?
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Fitting input data into Gaussian distribution

I'm currently reading papers on Variational Autoencoders (VAE). According to this article (http://proceedings.mlr.press/v95/guo18a/guo18a.pdf): By fitting the input data sample x(i) into the Gaussian ...
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36 views

Keras: Very high loss for Autoencoder

I am trying to implement an autoencoder for prediction of multiple labels using Keras. This is a snippet: ...
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Why is the posterior chosen to be normal in variational autoencoder?

Is there any reason for choosing the posterior $q(z|x)$ as normal distribution in variational autoencoder? or is it just for convenience?
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Input pipeline with an autoencoder and tf.data

I am using an autoencoder to detect anomalies in dataset of network traffic. The dataset is a csv file, and is loaded and preprocessed with pandas (encoded categorical features with pandas.get_dummies(...
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Unused bottleneck neurons in autoencoder

I'm using an autoencoder to compress categorical data, by a factor of about 20x. For this part of my data set, I have roughly 3500 variables, so my final bottleneck size is 180. I'm getting pretty ...
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How to minimalize noise from autoencoder output in audio reconstruction

I am training an autoencoder that takes an audio sample and outputs a variation of the input. The network is working as expected by the final output contains noise and it's not very clear. I am ...
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45 views

Help needed in interpreting the loss, val_loss vs epoch plots for an autoencoder training?

I am training a variational autoencoder and I am getting a loss-plot as follows: Rigt after epoch 224, val-loss overtakes train-loss and sort of getting bigger but at an extremely slow pace as you ...

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