Questions tagged [autoencoder]

Autoencoders are a type of neural network that learns a useful encoding for data.

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Encoding Python [duplicate]

I have a data set like ...
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Fully Convolutional Variational Autoencoder

I would like to make a neural network which uses black and white images as input and outputs a colored version of it. The important thing in that process is that the size of the images must stay the ...
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What is the meaning of constraint two neural networks against each other?

I have two identical autoencoders with the same number of layers and parameters. I would like to find the similarity between two images from different domains such as an image captured from the camera ...
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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 ...
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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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Autoencoder for clustering

I would like to know if the following strategy could work. I want to cluster images, using the following 2 steps: Reduce image dimension with autoencoder apply clustering algorithm like k-means I ...
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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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Post Training Sampling from a unit Gaussian?

During the training of a VAE, we sample Z from N(mu, sigma) ( = Q(Z|X) ) and then feed it to the decoder for reconstruction. Why do we, then, feed Z ~ N(0,1) to the decoder after the training is ...
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variational autoencoder doesn't fit with KL loss Keras

Hi I am learning the variational autoencoders and Idesigned my VAE as follow: ...
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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 we overfit a CNN AutoEncoder for anomaly detection?

I have been working on an anomaly detection problem in which I need to treat the images of "street" as an anomaly. The images of "glaciers" will be treated as not-anomaly. The autoencoder which ...
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Keras / TensorFlow 2.0: are UpSampling2D() layers the inverse of Max-Pooling?

I am trying to build a Variational Autoencoder for image data. As I employ MaxPool2D() in the encoding part, I need the reverse it in the decoder. Are ...
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Is my Autoencoder architecture correct?

I am dipping my toe in deep learning, especially autoencoders. I am trying to reconstruct my dataset. The dimension of my dataset is 13000x3. Can the following architecture work? ...
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Using smote for data augmentation of a data set which has no dependent variable

I am trying to use the reconstruction error obtained using an auto encoder to do novelty detection. My data set is of size (4500,55)(Note: this data doesn't have any abnormalities.When an auto-encoder ...
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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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Replication of Andrew Ng's Sparse Autoencoder

for the past three days I have been trying to replicate the results presented in Andrew Ng's sparse autoencoding lecture (https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf) however I have ...
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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 ...
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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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Design / Choice of Autoencoder to classify temporal pattern in images

Suppose I have a temporal stack of images of shape $m \times n \times k$ where shape of each image is $m \times n$ and $k$ represents the temporal dimension. In this context, I am trying to detect and ...
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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 ...
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Autoencoder not overfitting data after large number of epochs and small number of samples

I am training a deep autoeocoder on numerical data, with python jupyter notebook. I have 17 samples, each with 534 values, and my auto encoder has all layers to 534, but even after 5,000 epochs, the ...
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Application of Varitiational Auto Encoders in improving the generalizability of classifiers when there is limited amount of data?

Is there any prior work on the above topic? Currently, I am working on Domain Adversarial Neural Networks and merging it with VAE to improve generalizability and transfer learning. I could not find ...
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How to decode text output in autoencoders?

I have made an autoencoder for text based input, and fitted it to the data. Now I want to see the output text. Is there any way to decode the numbers to text? ...
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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. Kindly help. And I would also like to ask if label-encoding this feature would not ...
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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 ...
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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 ...
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LSTM -Detect single point in time series

I know that LSTMs can learn dependencies for many variables across many timestamps easily. I have used LSTMs to forecast the time series. Now my Aim is to identify when the process is getting ...
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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 ...
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What machine learning model to predict a sequence of instructions from a matrix

So my task involves taking an input of variable sizes convolutional filters and predicting the sequence of instructions for a processor to implement that filter. Now the sequence can be of variable ...
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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 ...
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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. ...
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Can I make it 'return _sequences = False' in autoencoder, using Keras?

Question Should we use ‘return_sequences=True’ for all the LSTM layers in the encoder of the LSTM autoencoder? My questions are based on the article LSTM Autoencoder for Extreme Rare Event ...
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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 ...
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Why maximize ELBO in the variational autoencoder?

For a variational autoencoder, we have that: $$\mathcal{L}(x,\theta,\phi) := \mathbb{E}_{q_\phi(z|x)}[\log p_{\theta}(x|z)] -KL[q_{\phi}(z|x) ||p(z)] $$ This is called the variational lower bound or ...
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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 ...
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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 ...
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Why does Logistic Regression perform better than Autoencoders when classifying imbalanced data?

The 'shuttle' data can be downloaded from the link here. It is imbalanced data and there are two classes in the target variable. The proportion of the two classes are seven percent. I used Logistic ...
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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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Autoencoder and Dimensionality

I'm pretty confused with the input/output/dense portion of an autoencoder. So my data consists of a numpy array of a 9 categorical features which have all been one hot encoded. So the input would ...
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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 ...
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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 ...
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Variational auto-encoders for text generation

How are the variational auto-encoders used for text generation? Can variational auto-encoders be used for character based text generation?
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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, ...
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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 ...
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InvalidArgumentError: incompatible shapes: [32,153] vs [32,5] , when using VAE

I'm working on a sequence to sequence model using LSTM, the model worked perfectly with an autoencoder, but when I try to use a Variational autoencoder by adding the mean and deviation layer and ...