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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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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14 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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101 views

Improve Convolutional Autoencoder

I just built a Convolutional Autoencoder to try to reconstruct a time series with shape (4000, 10, 30). This is the code, I used a batch size of 32, but I think it ...
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Autenocoder and anomaly detection task

I'm trying to create an autoencoder for the anomaly detection task, but I'm noticing that even if it performs very well on the training set, it starts to stop recreating half of the test set. I tried ...
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How to apply variational autoencoder for oversampling with cross-validation?

Currently, I have an imbalanced data set with proportions 84% and 16%. I wanna use VAE as oversampling method and I want to determine the best proportions of data that results in better metrics. Also, ...
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26 views

Understanding time series anomaly detection using Autoencoder

I'm studying how to detect anomalies in the time series using an Autoeconder. In particular, I'm following the guide posted in the Keras website, but I don't understand why they are creating and how ...
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Dimensionality reduction for geometric curves using an autoencoder - what is wrong?

I am trying to play with toy models in order to study autoencoders. In particular, I want to do dimensionality reduction for simple geometric curves in 3D. First, I take a toroidal helix. ...
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16 views

What is the correct terminology for neural network architectures that expand in their internal layers? (The opposite of a “bottleneck”)

I am aware that an auto-encoder contracts in its hidden layers to form a bottleneck. In contrast to this, is there a good name for the kind of cell, block or architecture that expands in its internal ...
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28 views

Convolutional AE always overfitting time series - what’s wrong?

I've build a CAE for anomaly detection in time series, but it is always overfitting. I've tried data augmentation, short/long inputvector, dropout rates... I don't know what I'm doing wrong, may be ...
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30 views

How does an autoencoder 'fill in the blanks' in the context of a recommender system?

My understanding is that an autoencoder takes an input, produces a lower dimensional representation of the input, which should explain the original features in the dataset, and then reconstructs the ...
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Is there any problem with the following Python+TF+Keras code for a custom loss function and network?

I am trying to code a custom loss function for variational autoencoder. I am not using mse for reconstruction loss since I am not learning p(x|z) ~ N(mu,I). Instead ...
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1D CNN Variational Autoencoder Conv1D Size

I am trying to create a 1D variational autoencoder to take in a 931x1 vector as input, but I have been having trouble with two things: Getting the output size of 931, since maxpooling and upsampling ...
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Autoencoder implementation using ImageDataGenerator

I'm using the concept demonstrated in this paper. Their training data consists of "GOOD" images and "BAD" images. They train the AE using "BAD" images (X) to make it ...
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Use convolutional variational autoencoders for time series prediction

I want to use convolutional variational autoencoders for time series prediction. For example, here is the dimension of my data. ...
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32 views

How to convert the reconstruction error from a variational autoencoder into a probability score?

I am faced with a dilemma while working with a variational autoencoder model. I am thinking of converting the reconstruction error into a probability score (aim is to use to determine anomalies ...
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26 views

Dimensionality reduction convolutional autoencoders

I don't understand how convolutional autoencoders achieve dimensionality reduction. For FFNN based autoencoder, the reduction is easy to understand: the input layer has N neurons, and the hidden ones ...
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Is Dynamic Time Warping a good loss function for a time series auto-encoder?

I've been trying to implement a multivariate time-series auto encoder. I thought DTW could be a good loss function but my implementation is still too slow. Anyone has some ideas of pros and cons of ...
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Reversing data through a tensorflow feature_column.embedding_column

I am building an variational autoencoder in Tensorflow, and one of my columns has object data. The data is too sparse (on the order of 2^16 possible values) to use one-hot encoding, it's not ordinal ...
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How to get all data from an ImageDataGenerator?

I want to get the result of all the processing that the generators performed on my images. For example: x,y = generator.next() but for all data, not just one batch (...
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Can I replace an embedding layer with hot encoding layer?

I'd like to build an autoencoder for a movie recommendation. My data looks like below user, movie, rating 1, 123, 3 1, 333, 5 2, 34, 3 ... I have seen people using ...
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How to do feature reduction for a log-linear regression model

I'm building a log-linear regression model and I have 18 different variables in my model. 13 out of 18 variables I'm using are hot-encoded variables for holiday, e.g. showing which holiday it is. I ...
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Reconstructing an audio signal from its Mel-scale spectrogram using an autoencoder

I'm looking for some papers/references that attempted to reconstruct audio signals from their Mel-scale spectrograms using an autoencoder or other neural network. I am thinking of training the ...
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Single real number evaluation metric for VAE for a regression problem

I've set up a VAE for a regression problem. ...
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20 views

Auto encoders to convert black and white image? [closed]

I am trying to get an intuitive understanding of auto encoders and how it works colorizing B/W images my questions are, What kind of input data and test data is needed? What will be the class labels? ...
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How to cross validate WDBC.csv breast cancer classification dataset in Stacked Autoencoders? [closed]

I need kind guidance regarding the context of how to cross validate WDBC.csv (Wisconsin Breast cancer diagnostic) dataset for breast cancer binary classification in Stacked Autoencoders as I put the ...
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MSE errors on autoencoder for dim reduction decreases in a weird patteren and I would love some help to dechyper it

I'm training a denoising autoencoder right now to reduce the dimension of a feature vector I designed of dim 58 to a latent space of dim 10, or less hopefully. I'm having a hard time understanding ...
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1answer
36 views

Encode time-series of different lengths with keras

I have time-series as my data (one time-series per training example). I would like to encode the data within these series in a fixed-length vector of features using a keras model. The problem is that ...
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56 views

using simple autoencoder for feature selection

I am using a simple autoencoder to extract the informative features and I have multiple Q: I know that the features extracted will be a linear combination of the original features so I consider that ...
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Weird cutdown in VAE mean graph

I'm experimenting with VAE with Tensorflow, and just following tutorial setups to have an approximate comparison. Well, they aren't similar at all. Tutorial mean graph: My mean graph: As you can see ...
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Which is the best way to select categorical features with Autoencoders in Python?

I have a dataset containing both categorical and numerical features. I am trying to work with Autoencoders for feature selection, so the first thing I do is to normalise the numerical features. For ...
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1answer
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Does autoencoder with few images not work?

I am trying to load 2 images to an autoencoder but for some reason, it does not rebuild the input image. An autoencoder is supposed to compress and decompress an image. However, when passing two ...
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1answer
28 views

Image regression problem

I've tried a number of experiments with machine learning. From trying to use GANs to upscale images to playing with auto-encoders. There is one problem that haunts me and always ends up ruining my ...
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29 views

loss function of autoencoders

I am trying to understand the loss function of autoencoders. I think the loss function of the decoder is to compare the input image with the decoded image, tell me if I am correct. but I don't ...
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How does bottleneck layer reduce computations without compromising with performance?

I was reading an article explaining the google's inception model . There it was mentioned , that to reduce the number of computations , we use a bottleneck layer. But I was surprised , if the model ...
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Does the Context Vector consist of hidden state and Cell State or just the hidden state?

LSTM's carry a hidden state and a cell state with them. Now, in a standard encoder-decoder model, we pass the Context Vector from the encoder to the decoder. Does, this Context Vector consist of just ...
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Autoencoder for Extremely Sparse Data

I am attempting to train an autoencoder on data that is extremely sparse. Each datapoint is only zeros and ones and contains ~3% 1s. Being that the data is mostly zero the autoencoder learns to ...
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257 views

Variational Autoencoder with custom loss in Keras giving “nan” loss while training

I am trying to write a simple Variational Autoencoder for a numerical dataset as opposed to images such as MNIST etc. I am basically replicating the keras blog post on this subject (with obvious ...
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1answer
31 views

Can we use feature selection and dimensionality reduction together?

I have a dataset having about 10,000s of features. The features have a hierarchy inherent to them. I found an algorithm performing feature engineering, taking the hierarchy of the features into ...
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Deep Continious Clustering algorithm - just one output cluster

I use the DCC algorithm to cluster some data. The whole algorithm is available here, but shortly it is: construct mkNN graph of the data points (the connected components of it are the clusters). ...
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Sampling for the encoder part of the VAE

my question regards the code utilized to implement the sampling function in the encoder part of VAE. Supposing that we chose a latent dimension of 2. Before the latent representation, we have 4 ...
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1answer
252 views

KL divergence loss goes to zero while training VAE

I'm trying to train a variational autoencoder to perform unsupervised classification of astronomical images (they are of size 63x63 pixels). I'm using an encoder with 2 convolutional layers and a ...
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Do all autoencoders perform dimensionality reduction [closed]

I want to use Convolutional autoencoder to find patterns in data as well as reduce dimensions. Can it be used for this purpose? Moreover, is removal of multicollinear features through autoencoder ...
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Training a Variational Autoencoder (VAE) for Non-Uniform Random Number Generation

I have a complicated 20-dimensional non-uniform distribution and would like generate samples from it. I have considered training a VAE to do so, but my problems are the following: Is my approach ...
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1answer
284 views

How to Save Model that has a TensorFlow Probability Regularizer?

Consider the following minimal VAE: ...
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1answer
84 views

Why KL Divergence instead of Cross-entropy in VAE

I understand how KL divergence provides us with a measure of how one probability distribution is different from a second, reference probability distribution. But why are they particularly used (...
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1answer
34 views

Is window based sequencing a good idea to obtain more training data for LSTMs?

I am trying to do an unsupervised autoencoder based outlier detection for time series using LSTMs. Here, there are multiple time series, and an entire series is to be considered as an outlier. However,...
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36 views

Varitional Autoencoder not accepting batch size or validation data

The input to the VAE will be a customer vector where the index of the vector represents a product id, position i in vector x is set to one iff product id i has been purchased by the customer. For ...
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7 views

What affects blurriness of images outputted by vanilla VAE?

I know that MSE between reconstruction (m(z)) and original image (X) affects blurriness, because it averages across pixel values. I also know that variance of the Gaussian distribution p(x|z) (p(x|z) =...
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134 views

Using autoencoder for time series prediction

I was recently reading a paper on time series prediction using deep learning methods. There I found a technique named "Variational Autoencoder" to predict time series data. I understand how ...
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1answer
60 views

Training a Variational Autoencoder (VAE) for Random Number Generation

I have a complicated 20-dimensional multi-modal distribution and consider training a VAE to learn an approximation of it using 2000 samples. But particularly, with the aim to subsequently generate ...

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