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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Tensorflow.js - CNN/or autoencoder denoiser architecture?
I am new to machine learning. I have 10,000 examples of 128x256 array of values 0.0-1.0. Each example consists of a pair of a clean example and the other with noise added. I am aiming to train a CNN / ...
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What model and attributes would be good for this data?
I have the following set of data like in the picture, with 366 Temperature values for one year. The first set of data would be for training and the second one for test. I would like to detect the ...
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How to detect anomalies?
I have timeseries data with one value per day for a year. (there is one column with temperature data). I am using autoencoders to train a reconstruction model with mse loss.
Firstly, I normalized the ...
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Auto encoder network
Is there any rule that we should use only deconvolution operations in decoder block of auto encoder network or we can use convolution in such way that it up-samples or mirrors the corresponding ...
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How to improve L2 loss for generative autoencoder
I am working with a modified generative autoencoder and having issues getting the L2 sufficiently low.
I think problem is that because my data is over a very large range and is standardized to values ...
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Training data for anomaly detection using LSTM Autoencoder
I am building an time-series anomaly detection engine using LSTM autoencoder. I read this article where the author suggests to train the model on clean data only in response to a comment. However, in ...
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LSTM Autoencoders vs LSTM
I'm working on a time-series anomaly detection project. I have read that both LSTM Autoencoders and LSTM can do the job. Can someone please help me understand what are the advantages of each i.e. when ...
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LSTM Auto-encoder Implementation
I'm trying to implement an LSTM auto-encoder in PyTorch for time-series data (univariate and/or multi-variate). Initially, I assumed it would be fairly easy but I realised there are a few ...
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Can I use Variational Autoencoder/GAN for image manipulation?
I have a CT image with the tumor and the corresponding Radiotherapy image. I want to predict the CT-Image with the corresponding change. For my training, I do have input CT image, Radiation therapy ...
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Automatic feature extraction from EMG, autoencoder vs variational autoencoder?
Thanks for checking my question!
From the EMG signals of Parkinon's patients, I want to extract rigidity and bradykinesia information. In order to do that, we need feature engineering.
But, I would ...
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LSTM Autoencoder set-up for multiple features using Pytorch
I am building an LSTM autoencoder to denoise signals and will take more than 1 feature as it's input.
I have setup the model Encoder part as follows which works for single feature inputs (i.e. ...
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Incremental learning on Autoencoder for anomaly detection
I want to incrementally train my pre-trained autoencoder model on data being received every minute. Based on this thread, successive calls to model.fit will incrementally train the model. However, the ...
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Does Sliced Wasserstein Distance work in higher than 2 dimensions?
I had thought that it only worked for 2D distributions.
I am trying to implement a sliced Wasserstein autoencoder and I was wondering if my latent space can be larger than 2D.
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Latent space vs Embedding space | Are they same?
I am going through variational autoencoders and it is mentioned that:
continuity (two close points in the latent space should not give two
completely different contents once decoded) and completeness ...
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Re-train the decoder of an autoencoder?
Can the decoder of the pre-trained autoencoder be trained again by taking the feature vector of the Siamese Neural Network (SNN) as input?
I have trained the SNN model. Additionally, I trained ...
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Keras autoencoder gives same output
I have a convolutional auto-encoder which is created like this:
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Variational Autoencoder assumtions
I am currently reading the paper "Importance Weighted Autoencoders" and am having a hard time understanding something regarding the original Variational Autoencoder (VAE) as described here
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Importance Weighted Autoencoder update rule
I am currently reading the paper "Importance Weighted Autoencoders" and am having a hard time grasping something.
Firstly, the paper discusses a proposed upgraded lower bound to the original ...
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Build autoencoder for single matrix with integer numbers
Can you please tell me how to build an autoencoder with a single matrix(4,4) with integer numbers?
I want to build an autoencoder for the below-mentioned data. I don't know whether I should convert ...
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Loss value in Autoencoder neural network
We have an autoencoder neural network and we get a loss value (loss = binary cross entropy) convergent to a certain number.
What can we deduce from this value ? Is this value strictly related to the ...
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Generate new distribution from auto-encoder /variational autoencoder
I know that autoencoders can be used to generate new data.
From what I could understand..
The autoencoder uses the original distribution X to learn a random gaussian distribution described by mean and ...
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Variational Autoencoders VS Transformers
I'm relatively new to the field, but I'd like to know how do variational autoencoders fare compared to transformers?
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CNN auto-encoder performs much worse than Fully connected auto-encoder
I am trying to develop a one-class classifier, that will learn regular examples, and, hopefully, will have hard times reconstructing anomaly observations.
I have 1D signals which I tried to ...
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Reference code for LSTM Variational Autoencoder for dimensionality reduction
I have time series data, with many features.
I would like to reduce the dimentionality by using LSTM VAE.
Does anybody know an example code or a reference to guide me to impolement it?
Both Pytorch ...
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Variational autoencoder for time series denoising and dimentionality reduction
I have a dataset X of multiple series say 100 (size=100).
I would like to use VAE to both denoise the data and reduce the dimensions to a smaller latent space Z (size Z << size X), because I ...
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Expectation of ELBO in Variational Autoencoder
I am working with VAEs. My input is x, which is a product of two variables $x_1$ and $x_2$.
The objective (ELBO) of VAE in terms of x is:
$E_{z\sim Q}[\log P(x|z)] - \mathcal{D}[Q(z|x)||P(z)]$.
I want ...
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Can we use autoencoders to change an existing image instead of create one from scratch?
I'm trying to think if we can use autoencoders to edit an existing image instead of saying, creating a new one from scratch.
To give an example, say I train my data on the MNIST dataset. If I now give ...
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How should I think when I want to compare mu and sigma for different images in VAE?
I'm searching for a way to compare mu and sigma values of the encoder network's output of variational autoencoders.
In detail, imagine I trained my VAE on the MNIST digits dataset using the official ...
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What does it means (concretly) that a VAE encode inputs as distribution?
From this post we can read that VAEs encode inputs as distributions instead of simple points ?
What does it mean concretely ? If the encoder consists of the weights between the input image and the ...
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quereies related to autoencoder
i want to design an deep auto encoder after following keras tutorial.
Input is a simple 2-dimensional image consists of 512 rows and 50 columns matrix
My trial code is
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How to shape data when using LSTM autoencoder
I am working with simple data that has multiple features and a time stamp column. I have 24 hours of data across 70 days. The total number of samples is 1680.
When applying a LSTM autoencoder, how ...
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how an autoencoder denoise an image
i am using denoising autoencoder to denoise the image in the unsupervised way.But still after implementation of the denoisng autoencoder i am unable to understand how an autoencoder network know which ...
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how to set threshold value by looking at loss distribution in anomaly detection task
I am following this tutorial https://towardsdatascience.com/lstm-autoencoder-for-anomaly-detection-e1f4f2ee7ccf to use LSTM autoencoder to detect anomalies in my unsupervised dataset. they plotted ...
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What is the reason of this behavior of training loss in CONV auto-encoders?
I don't get Training Loss is steady up to the 7th epochs
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Deep learning model for very sparse object detection in noisy images
I am trying to build a model that takes in very noisy 200x200 greyscale images of spatially sparse objects and attempts to localise them with bounding boxes. The objects are very thin streaks (data of ...
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ValueError: Input 0 of layer conv2d is incompatible with the layer: expected axis -1 of input shape to have value 1 but received input with shape
I'm trying to create an auto-encoder based model for segmentation, which looks something like this: https://i.stack.imgur.com/4F3Z0.png
I haven't added a single step, nor missed one as far as I ...
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Model accuracy and validation accuracy stuck at constant value for denoising autoencoder model
I am building a denoising autoencoder (DAE) to denoise respiratory signals. I pass through the model both noisy and clean versions of the signal (in frame sizes as multiples of 1024).
I've set up my ...
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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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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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