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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Which algorithm can be used to reduce dimension of multiple time series?

In my dataset, a data point is essentially a Time series of 6 feature over a year per month so in all, it results in 6*12=72 features. I need to find class outliers so I perform dimensionality ...
Faiz Kidwai's user avatar
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Comparison of performance of autoencoder with PCA

I am running PCA and autoencoder (2 hidden layer with relu) on a data. Both PCA and autoencoder give similar accuracy of the order 50%. I have tried different variations of autoencoder: changing ...
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How to calculate a location prior in CT's based on atlas segmentation?

I'm working myself through the paper "Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation" (https://arxiv.org/abs/1903.03148) I lack to understand how the atlas ...
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How can one be assured that generative models are not memorizing dataset, and that they will generate an unique image outside of dataset?

If all GAN can do is capture the probability distribution of the dataset, then shouldn't they be similar to handing out images from the dataset? How can we verify that the images that they generate ...
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Developing an encoder/decoder for image modification

On the project I am currently working on, my goal is to train a neural network to convert images of circles to ellipses in a way that models convolution/blurring in real imaging processes. What ...
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Optimizing parameters for CNN autoencoder based on training and validation loss

I have designed an autoencoder with a encoder and decoder consiting of 2D convolutational layers (the input are 40'000 2D images). I train the autoencoder using adam optimizer. The autoencoders has ...
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How do I get similarity with autoencoders

I have build an autoencoder to extract from a very high dimensional (200 dimensions) space a smaller but significant representation (16 dimensions). Now that I have these "encoded" vectors, I would ...
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Autoencoder feature extraction without validation set?

I plan to use autoencoder for feature extraction, then use the latent vector for clustering. My autoencoder performs very very well on my training set (loss small and reconstructed image look very ...
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Filters in convolutional autoencoders

I have a question regarding the number of filters in a convolutional Autoencoder. As far as I have understood, as the network gets deeper, the amount of filters in the ...
Michael Lempart's user avatar
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Can autoencoders take time series into account?

Here, I read the following: The first key to understanding is that HTM relies on data that streams over time (...) By contrast, conventional deep learning uses static data and is therefore time ...
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Does it make sense to train an Autoencoder for Dimensionality Reduction using Mini-Batch Gradient Descent?

I want to reduce the dimensionality of a dataset using a stacked Autoencoder. The size of the dataset and the computing power at my disposal make it very difficult to train the Network using simple, ...
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Resize instead of transposed convolutions

I'm trying to build a decoder version of ResNet, i.e. one that goes from the prelogits layer and attempts to recreate the image. I can get it working by using transposed convolutions, but the quality ...
Kurt Newman's user avatar
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String indices must be integers

I was trying to encode the string values of the feature 'ProductCategory' into integer values, but I got this error. Also, I would like to ask if label-encoding ...
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How to calculate compression ratio when using autoencoder in neural network

For example, if I use an autoencoder to compress a 1000 dimensional data set to 25 dimensions. Is the compression ratio is 40:1? Other info: The dataset contains 5000 samples. 2 million parameters ...
Ellen Sheldon's user avatar
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What is the difference between an autoencoder and an encoder-decoder?

I want to know if there is a difference between an autoencoder and an encoder-decoder.
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Why do we use a softmax activation function in Convolutional Autoencoders?

I have been working on an image segmentation project where I have created a convolutional autoencoder. I saw this image and implemented it using Keras. At the output layer, the author has used the ...
Shubham Panchal's user avatar
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Intractability in Variational Autoencoders

I'm having difficulty understanding when integrals are intractable in variational inference problems. In a variational autoencoder with observation $x$ and latent variable $z$ we want to maximize data ...
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How to scale outputs from AutoEncoder from multiple models?

I have a problem for which I have not been able to find any answers in my search so far. BACKGROUND I am working on an anomaly detection problem on machines utilising an auto-encoder. I am building ...
DaytaSigntist's user avatar
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Autoencoder gets ~0% accuracy / doesn't train at all

So I wanted to get into the topic of 'Autoencoder', and just tested how well it would work on random vectors of size 200. ...
Marie M.'s user avatar
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How smaller does the input data get reduced in a LSTM autoencoder

Question In a LSTM autoencder, how smaller does my input data(59 features) get reduced in a latent vector, which is usually located in the middle between an encoder and a decoder? Why did the ...
Eiffelbear's user avatar
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Why maximize ELBO in the variational autoencoder?

For a variational autoencoder, we have that: $$\mathcal{L}(x,\theta,\phi) := \mathbb{E}_{z \sim q_\phi(z|x)}[\log p_{\theta}(x|z)] -KL[q_{\phi}(z|x) ||p(z)] $$ This is called the variational lower ...
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Large scale Autoencoder for friendship recommendation

I have a user friendship graph for about 30 Million users. I am trying to use an auto encoder [30 Million, 512, 512, 1024, Dropout(0.3), 512, 512, 30 Million]. But I am not learning anything. Has ...
Veshnu Ramakrishnan's user avatar
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How to check quality of latent space like in β-VAE article?

There is a nice plot in the β-VAE article that shows quality of latent space code: Is there a general way to visualize or analyze latent space code dimensions so that is would be clear if they are ...
Peter Zagubisalo's user avatar
2 votes
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IndexError: list index out of range

I'm implementing a sequence-2-sequence model with RNN-VAE architecture, and I use an attention mechanism. I have problem in the decoder part. I'm struggling with this error: IndexError: list index ...
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Shouldn't an autoencoder with #(neurons in hidden layer) = #(neurons in input layer) be "perfect"?

I'm exploring autoencoders for the first time. I'm using the Matlab neural networks toolbox. I have created a synthetic dataset consisting of points in 2D space plus some noise. My idea was to ...
Rafael March's user avatar
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What is the best architecture for Auto-Encoder for image reconstruction?

I am trying to use Convultional Auto-Encoder for its latent space (embedding layer), specifically, I want to use the embedding for K-nearest neighbor search in the latent space (similar idea to ...
Idan Azuri's user avatar
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1 answer
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How to explain get_weight with autoencoder in keras?

I built an autoencoder model of three layers with 9 5 9. Input dim =9, encoder dim =5, output dim=9 When I get the model weights, ...
disney82231's user avatar
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What do we visualize in showing a VAE latent space?

I am trying to wrap my head around VAE's and have trouble understanding what is being visualized when people make scatter plots of the latent space. I think I understand the bottleneck concept; we go ...
ITA's user avatar
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What is "posterior collapse" phenomenon?

I was going through this paper on Towards Text Generation with Adversarially Learned Neural Outlines and it states why the VAEs are hard to train for text generation due to this problem. The paper ...
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How is the standard deviation of VAE's obtained?

I am trying to build a Variational Autoencoder. I was looking at various codes online and found most of them in some way or another copy Francois Chollet (Google researchers) code. Now my main ...
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Variational AutoEncoder giving negative loss

I'm learning about variational autoencoders and I've implemented a simple example in keras, model summary below. I've copied the loss function from one of Francois Chollet's blog posts and I'm getting ...
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Why use Variational Autoencoders VAE instead of Autoencoders AE in Anomaly Detection?

I have read many papers that recommend using Variational Autoencoders over Autoencoders since they have a more probabilistic approach and the ability to use KL divergence on the latent dimension. But ...
Jack Farah's user avatar
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Understanding ELBO Learning Dynamics for VAE?

As I understand it, I'm basically minimizing the KL Divergence between the Prior and Encoder latent distribution, and the log probability of the decoder distribution. I have a model that does generate ...
andrew's user avatar
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Tensor Shape 'Reshape' during the dataflow

I would like to combine an Autoencoder with a LSTM. However, the 'timestep' is a block for the implements and I would like to train them together. Is there a solution to the tensor 'reshape'? I mean, ...
Jie Wang's user avatar
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What is the difference between KL-divergence, JS-divergence, Wasserstein distance and MMD?

I was reading about different distribution distances, and came across Kullback-Leibler divergence Jensen-Shannon divergence Wasserstein distance Maximum mean discrepancy (MMD) The book was too ...
asahi kibou's user avatar
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Variational auto-encoders (VAE): why the random sample?

Why do people train variational auto-encoders (VAE) to encode means and variances (regularised towards 0 and 1), and then sample a random Gaussian, rather that simply encode latent vectors and ...
Antoine Savine's user avatar
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Help required to implement the below model using Bi-GRU

As you can see in above images I need to model Bi-GRUs stacked as shown in table which takes input (N,1,64) and outputs (N,204). The input data is binary number stream and so is output data. Can ...
Sank_BE's user avatar
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what is correct way to perform normalization on data in Auto encoder?

working on anomaly detection problem. i'm using auto-encoder to denoise given input. I trained network with normal data(anomaly free). so model predict normal state of given input. Normalization of ...
Milan_Harkhani's user avatar
9 votes
2 answers
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Transform an Autoencoder to a Variational Autoencoder?

I would like to compare the training by an Autoencoder and a variational autoencoder. I have already run the traing using AE. I would like to know if it's possible to transform this AE into a VAE and ...
Kahina's user avatar
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How to set the Reconstruction error threshold for anomaly detection using autoencoders?

Hi I am doing anomaly detection using auto encoders.I have trained the model using 'Non Anomalous' values.Now when I give anomalous points as test data. What should be the Reconstruction error ...
Fasty's user avatar
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Autoencoder Dimensionality Error

Thanks for taking a look! I have an auto-encoder that I am trying to use for anomaly detection. I have 2 log files, logfile.log and testfile.log. They're essentially the same logfile, I just split ...
NeedingMLHelp's user avatar
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Train autoencoder on 1D data with high and low amplitudes

I'm using an autoencoder-RNN combination to predict colliding waves in 1D. The training data is obtained by using the 1D Euler equations along with boundary conditions to preduce to waves (left and ...
Boris Mulder's user avatar
1 vote
1 answer
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Autoencoder doesn't learn to reduce dimesions

I coded a neural network from scratch in Python. I tried it with the XOR problem and it learned correctly. So I tried to encode an Autoencoder with 3 inputs (and therefore with also 3 outputs) to ...
Giuseppe Romeo's user avatar
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Any heuristic for minimal DCGAN latent space dimension?

I am highly interested in approaching minimal latent space dimension (as many other may be) for DCGANs or autoencoders. In this example of DCGAN on the MNIST dataset, the person uses a 100-...
Nicolas Scotto Di Perto's user avatar
2 votes
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78 views

Build an Autocomplete model for document titles

I want to build an autocomplete model using RNN where input is article names (documents title). X: ['Billing', 'Loan status', 'Filling loan application', 'Contact Info', ...] The article name can ...
quintin's user avatar
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KL divergence in VAE

If I understand correctly KL-divergence is relative entropy of two distributions. To calculate KL divergence of two distributions, you would need two vectors of random variables. What I do not ...
Stenga's user avatar
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1 answer
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Training multiple keras models and combining outputs to determine losses

I'm trying to predict the future states of a 1D travelling wave (square, triangle and sawtooth) using a deep learning setup in Keras. The waves are discretised in a 1024 data points. As this gives a ...
Boris Mulder's user avatar
1 vote
1 answer
463 views

Training 128x128 autoencoders on 512x512 images, produces strange gridline after recombining

So I'm training an autoencoder that can recreate 128x128 images, so it can recreate any images by splitting them into 128x128 patches first, running it through the autoencoder, and having them ...
Chan Woo's user avatar
1 vote
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What is the mathematical definition of the latent space [closed]

I have been seeking a mathematical explanation of the latent space of a neural network. The best I have gotten is "The latent space is the space in which the data lies in the bottleneck layer." ...
midawn98's user avatar
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Do anomalous input features to autoencoder result in high errors on the corresponding output features?

An autoencoder is trained by replicating each training instance to both input and output. However, when predicting for anomaly detection, will the output error be local to the same output feature(s) ...
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