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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Generating artificial training data with encoder and classical algorithm
I would like to know if this idea has been tried before, and if so, where I can find more information about it.
This is an approach to generating artificial training data for segmentation tasks using ...
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autoencoder feature extraction
I'm very new to deep learning and come across an idea of feature extraction by using autoencoder. I went through many example online and came across these following.
https://www.thepythoncode.com/...
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Larger Latent Space Dim for Point Cloud Autoencoder
So I'm trying to follow a paper that uses a AE to learn point clouds. The thing is, the dimension of the point cloud data is 3 (x, y, z), but the dimension of the latent space from what I can tell is ...
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Threshold for auto encoder anomaly detection
I have fitted an auto encoder on 25-dimensional time series data hoping to be able to detect anomalies. training set is 100k observations, testset for threshold setting is 10k observations. all ...
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Cluster-Based Anomaly Detection (and PCA)
I have a dataset of user operations (250 types of operations) in a trading platform and my task is to extract features, flagging rules, other insight for fraud prevention/anomaly detection, or some ...
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How can I show data clustering after building a Convolutional Variational Autoencoder?
I am brand new to Machine Learning, and just followed Tensorflow Convolutional Variational Autoencoder Tutorial using my own dataset instead of the MNIST set. I was able to successfully display the 2D ...
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Early anomaly detection / Failure prediction on time series
My problem here is that I want to predict failures in advance with respect to their occurrence. I have sensors mounted on my machine and with a certain frequency, they send data to my database. ...
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What is the dataset during testing a Variational auto-encoder?
I am getting confused in the testing dataset of a VAE. After training the VAE, what should be the testing data-set of the VAE?
I understand that during testing the VAE only has the decoder part. Hence,...
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Measuring the "smoothness" of the latent space of a (variational) autoencoder
Suppose I have an autoencoder (or variational autoencoder) that encodes language.
I want to compare different training regimes and evaluate the AE / VAE on how "smooth" its latent space is. ...
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Autoencoder: How should hidden layer be used?
I'm building a variational autoencoder to generate faces. I'm using gray-scale images with the size 30x30. I started with a very simple model:
Input Layer, 900 nodes, values 0-1
Latent Space, 10 nodes ...
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Feature extraction using autoencoder produces latent vector with large elements
Good day.
I am experimenting with a convolution autoencoder, represented below, for feature extraction. The latent vectors have large numbers, > 10 and < -10, that cause problems for later ...
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Python train convolutional on numerical values shape issue
I want to train a convolutional neural network autoencoder on a csv file which contains values pixel neighborhood position of an original image of 1024x1024. When I try to train it, I have the ...
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Why VAE Encoder outputs log variance and not standard deviation?
When talking about VAE (and viewing VAE implementations), the Encoder outputs:
μ, log(variance)
when we train the model (the ...
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Tips for making an autoencoder converge?
I am currently trying to make an autoencoder that compresses a 3D volume where each value represents the density of said volume. The architecture is a UNet without skip connections. The optimizer is ...
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Autoencoder Layers
I am using AutoEncoder to detect anomalies and my dataset is a numerical dataset that has 10 columns (including the target label),
I don't know what numbers I should choose for the first argument in ...
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Missing ReLU in autoencoders?
Hopefully this isn't too stupid of a question but when looking at implementing an autoencoder in PyTorch, there's something I find a bit confusing which makes me worry that I'm failing to understand ...
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Bibliographic References on Denoising Distributed Acoustic data with Deep Learning
Distributed Acoustic Sensing (DAS)
I have an iDAS (intelligent distributed acoustic sensing) dataset obtain from an undersea optical fibre. iDAS data have a 2D dimensional representation. On the one ...
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How to train encoder in BiGAN?
I have some difficulties training a BiGAN. In particular, the encoder seems not learning the map between the images x and the latent space z.
I have the following encoder:
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Manipulating weights after Keras concatenation
I'm trying to initialize the Autoencoder in the diagram as follows: At the beginning, I want all weights and biases to be zero except those that go directly from the input to the bottleneck, and ...
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How to improve the $R^2$ score on an autoencoder model when the loss(KLDivergence) and validation MAE is giving desired scores?
I have been training an autoencoder for data with 25k feats and 1k data points. The $R^2$ score is coming negative on every epoch, and around -27 on both train and test sets, although the MAE is 0.7 ...
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Autoencoders for figuring the underlying data distribution in python
I have the following randomly generated data
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curve interpolation/extrapolation
I am trying to get a model to "learn" possible shapes of a curve.
This curve moves with time and at each timestamp t I get a random sampling of 10 points (...
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VAE mean and Standard Deviations are input dependent?
The original presentation of variational autoencoders, VAE assumes the mean $\mu$ and the sd $\sigma$ are functions of the input variable, say $x$. I am studying this paper and came across the lines
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Is it possible to use text Auto Encoders without text generation?
I have a use case where I have large texts, and a lot of it. Pretty often the sequence length exceeds 1000 tokens. I need a lower dimensional compression of the texts as an input for a classifier. The ...
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Generative Autoencoder with latent vector size as a parameter?
I am interested in using a generative autoencoder (something like a VAE maybe) to sample very high dimensional data more easily (making use of the fact that the autoencoder reduces the dimensionality ...
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How to build Stacked Autoencoder using Keras?
Stacked Autoencoder
I have tried to create a stacked autoencoder using Keras but I couldn't do the last part of this autoencoder.
Here I have created three autoencoders. It works fine individually ...
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How is the encoder state passed to the decoder (LSTM, Keras)
My understanding is that in the Encoder Decoder LSTM, the decoder first state is same as the encoder final state (both hidden and cell states) . But I don't see that written explicitly in the code ...
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Autoencoder on spectral image ASCII data file
I am discovering Autoencoder CNN, I took a look around on tutorials, I see lot's of examples with images as input. I was wondering if it is possible to use files whose pixels are represented by Ascii ...
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Autoencoder vs Pre-trained network for feature extraction
I wanted to know if anyone has any sort of guidance on what is better for image classification on a lot of classes (about 400) with a small amount of samples per class (around 20), for relatively big ...
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tensorflow is not learning
I am writing this code from the tensorflow tutorial about Autoencoders
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Practical application of denoising autoencoders
I have been reading into autoencoders for the purpose of denoising data. In the examples i found (eg. [1, 2, 3], which are the first few google results) they have the following input/output:
Input ...
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Questions on GLM: General Language Model Pretraining with Autoregressive Blank Infilling
For GLM: General Language Model Pretraining with Autoregressive Blank Infilling ,
May I ask how is the sampling for input division in step (b) being done ?
why in step (c), the green ...
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What says the output of autoencoder?
What is the meaning of output of autuencoders? Can we say it is the noise removed version of actual dataset and should it be symmetrical?
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How can I use autoencoders for noise detection and removal
How can I use autoencoders for noise detection and removal in a dataset with only 2 features and no labels? How should my architecture be like, such as 2 1 1 1 2 or any other? And does the output of ...
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How to remove noise with autoencoders?
I have a dataset with only 2 features and without labels ans size 1 million. I need to detect noise and remove them with an autoencoder system. I have input layer with 2 node 1 hidden layer with only ...
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Trimming "unused" neurons from the bottleneck of an autoencoder
I'm working with autoencoding data in segments, and working with the latent space afterwards (I am also working on VAEs, but this segment of the project concerns deterministic AEs).
I've noticed that ...
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How to implement simple VAE with sparse tensor in Tensorflow
thanks for reading.
I have been attempting to train a simple VAE on very sparse 2D and 3D data. So far I have been training using dense tensors which - I think - is resulting in horrible training due ...
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Training a straight "copy the input to the output" autoencoder for audio is strangely slow
As a learning exercise, I'm training a "perfect" audio autoencoder. It has a hidden layer just as wide as the input layer, with linear activation. The expectation is that the network should ...
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Analyzing strange shaped predicted vs actual value graph of a predicted variable
I am training an autoencoder to compress some graph data to a latent space of n variables, and then training a neural network to predict some other structural data about that graph from the latent ...
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Deep learning model to predict the actual input values
I have some observed parameters to be used as input to the deep learning model. This problem comes from the wireless field where we transmit $x$. The channel $h$ is random in nature. The received ...
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Autoencoder general questions and poor loss
I'm trying to get a simple autoencoder working on the iris dataset to explore autoencoders at a basic level. I'm running into an issue where the loss of the model is extremely high (>20).
Can ...
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Mix of time-dependent and constant features for a transformer
I'm using the transformer architecture to predict future time-points from previous time-points.
Each item of the input sequence is a vector of ...
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KL divergence loss first decreases and then increases in VAE training
I am training a VAE on CelebA HQ (resized to 256x256). The training is going well, the reconstruction loss is decreasing and reconstructions are also meaningful. But, the problem is with KL divergence ...
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Using large CNNs (e.g., ResNet) in convolutional autoencoders for image representation learning
I am confused about which CNNs are generally used inside autoencoder architectures for learning image representations. Is it more common to use a large existing network like ResNet or VGG, or do most ...
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An autoencoder setup for anomaly detection
I am doing anomaly detection using machine learning. i have tried different models like isolation forest, SVM and KNN. The maximum accuracy that I can get from each of them is $80\%$ accordind to my ...
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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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