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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Issue with custom loss function including parameters in keras autoencoder

I am writing an Autoencoder that tries to find parameters for 3D Meshes. For example how bent an object is. I input the Mesh vertices but would like to include the true parameters versus the ...
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Creating a sub-model from pre-trained model

I have a pre-trained model having the following architecture: ...
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SPC vs Autoencoders in anomaly detection

Considering the usage of Autoencoders in anomaly detection of time-series data, why SPCs (control charts) have lost their charm? Are there any advantages with Autoencoders and disadvantages with SPCs?
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How to feed multiple asymmetric inputs to LSTM layer?

I'm trying to create an encoder-decoder architecture with an LSTM encoder. The intention is to use both the input image as well as the class label as inputs to the encoder, and to have them share the ...
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greater reconstruction error with Autoencoder while training on Google Colab

I am training an Autoencoder on Google Colab using the GPU. Every time I run the training afresh, the predicted result seems to have a greater image reconstruction error than the last time. The ...
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Can a convolutional neural network or an autoencoder deal with an input of complex values (complex numbers instead of real numbers)?

I saw in a model that they did consider the complex numbers as 2-D numbers before using Convolutional Neural Networks. However for the autoencoder, as much as i know, it can not deal with 3D, Am i ...
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Autoencoder for Dimensionality Reduction - varying result - parameter tuning

I'm not an expert in autoencoders or neural networks by any means, so forgive me if this is a silly question. The problem and steps taken to solve problem are as follows: There exists a data set with ...
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How to encode data for a semi-supervised Adversial Autoencoder (AAE)?

I'm trying to recreate the model described in section 5 of the AAE paper. I'm having trouble understanding the architecture of the encoder so I could incorporate both the input image and the class ...
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Summarize events per ID

Data: Each corresponds to an event (a person's visit to the hospital, as an example). I have a series of data associated with this event (duration of visit, motive, etc...). Objective: Summarize the ...
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Seq2Seq for sentence correction

I have a task in hand where I get a dirty formed sentence and need to correct it. Examples are, "StackOverflow best question answering platform" to be converted to "StackOverflow is best question ...
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Keras: DepthwiseConv3DTranspose or doing transposed Conv. with a Conv. layer

I am building an autoencoder for 3D images and would like to use Depthwise convolutions. For the encoder, I found an implementation of a depthwise 3D convolutional layer (DepthwiseConv3D). For the ...
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Adding random noise to latent representation increase the accuracy in the autoencoder

I am working on an autoencoder project, it consists of dense layers like this : ...
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Rendered Image Denoising

I am learning about "Image Denoising using Autoencoders". So, now I want to build and train a model. Hence, when I read into how Nvidia generated the dataset, I came across: We used about 1000 ...
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Chess deep learning siamese network overfitting when shouldn't in theory

TLDR: My network is training with pairs so instead of 10^6 samples it has 10^12 samples (The number of samples squared) . With that large of a data set is shouldn't overfit but it does after very few ...
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How to make sure that the learned weights are initialized instead of only layer structure?

I have trained a model for 100 epochs. The network is designed to save checkpoints after every 10th epoch. Besides this, once the training finished I saved the model using these commands: ...
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LSTM autoencoder reconstructs input in ascending order

I implemented an autoencoder LSTM using Keras just as indicated in this article: article. The problem is that the reconstructed input of the time-series is given in ascending order with respect to the ...
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Differences between autoencoders and dynamic time warping?

While they work quite differently in terms of implementation, the end result for unsupervised learning is quite similar: Dynamic time warping measures the distance between timeseries-like data (which ...
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What is the feedforward network in a transformer trained on?

After reading the 'Attention is all you need' article, I understand the general architecture of a transformer. However, it is unclear to me how the feed forward neural network learns. What I learned ...
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Analogy between Autoencoder and PCA

I know that Autoencoders can be regarded as non-linear generalisations of PCA, but I struggle to understand in depth the analogy between the two. Once PCA has been performed on a function $F(\vec{\...
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Auto-Encoder customized layer training

My question is related with model-weights optimization during back propagation. In this image I'm trying to represent an auto-encoder having 7 layers where 4th one is center layer. If my ...
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Keras Encoder - Decoder Model

Im training a model of 400 samples . The dataset contains 400 images of faces as input (X) and also 400 faces with glasses as output (Y) . im training the model by code below : ...
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Variational Autoencoder Latent Space size

Variational Autoencoder: Imagine we use a batch size of e.g. 32. Furthermore we got 2 Linear Layers (mu, sigma) which are 300 long. The output dimension of the encoder (conv2d layer) is (32, 64 , 64, ...
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Tensorflow2 graph tensor leaking when using layer.output

I am trying to build a Contractive Auto Encoder using Tensorflow 2.0. The model's loss function uses the encoder output in its calculations. The problem is that every time i retrieve the output, it ...
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Autoencoder for anomaly detection, output layer activation function

I am building an Autoencoder to detect anomalies. I have mixed data, i.e continuous and categorical. I have one-hot encoded the categorical data. Scaled the data with a MinMax scaler. To determine if ...
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InvalidArgumentError: Incompatible shapes while training

InvalidArgumentError: Incompatible shapes: [15,31744] vs. [15,31680][[{{node loss_4/output_loss/logistic_loss/mul}}]] Has anyone of you ever got this kind of ...
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How to train the machine so that it can give 'out of bound/classes' as an output for neural network

I know I was not able to word the title of the question properly. So I am trying to explain the problem here: Suppose, I built and trained a CNN to identify numbers from 0 to 9. However, when I ...
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Unable to transform (greatly performing) Autoencoder into Variational Autoencoder

Following the procedure described in this SO question, I am trying to transform my (greatly performing) convolutional Autoencoder into a Variational version of the same Autoencoder. As explained in ...
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Loss function for Autoencoder of sparse 3D Image

I have 3D structure data of molecules. I represented the atoms as points in a 100*100*100 grid and applied a gaussian blur to counter the sparseness. (nearly all of the grid cells contain zeros) I am ...
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Issue with implementation of Sequence to Sequence Autoencoder

Hi, I want to implement Sequence to Sequence Autoencoder for text message classification purpose whether it is spam or ham. The complete code is: ...
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How to correctly mirror / inverse an encoder architecture for the decoder?

For the purpose of autoencoders, It seems to be common practice to build the decoder architecture in a way so that it mirrors the encoder architecture. However, there seem to be 2 approaches of doing ...
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Dense output from neural network

I would like to create a loss function that encourages the output of the embedding of an autoencoder to be dense. I don't have an explicit condition for how density is defined, but one option would be ...
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What Non-linearities are best in Denoising RNN Autoencoders and where should the go?

I’m employing a denoising RNN autoencoder for a project relating to motion capture data. This is my first time using auto encoder architectures and I was just wondering what non-linearities should be ...
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Finding differences in smooth embedding spaces?

I'm trying to find statistically significant differences between embeddings (by an autoencoder) of different datasets. At first I tried to embed them separately, and cluster them. However, the ...
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How to extract features from the encoded layer of an autoencoder?

I have done some research on autoencoders, and I have come to understand that they can also be used for feature extraction (see this question on this site as an example). Most of the examples out ...
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How do I use Conv2DTranspose to create my decoder?

I am constructing VAE of input 80x60. I have my encoder below but I am troubles making the decoder as it does not conform to 80 x 60. Here is my decoder. Below is the code for constructing the ...
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Are Denoising Variational Autoencoders deterministic?

I have a pretty good understanding of regular autoencoders and, to a certain extent, of variational autoencoders, where the latent representation is forced to follow specific probabilistic ...
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How to use Variational Autoencoder's μ and σ with user-generated z?

My understanding of VAE is that unlike Autoencoders, it does not directly give you a discrete encoding (latent code vectors n-dim) instead, it gives you both mu and sigma (n- dim mean vectors and n-...
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Steps on how to use autoencoders to reduce dimensions

I have a dataset that contains text columns. I have used tf-idf to convert those text columns to numerical columns. I want to reduce the dimension of the dataset since tf-idf creates a multitude of ...
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How to estimate total correlation KL[q(z)||Πjq(zj)] of VAE after training (useful for latents disentanglement evaluation)

FactorVAE and β-TCVAE both use total correlation (TC) batch estimation for their objectives. Where TC is: $$ KL\bigl( q(z)||\prod\nolimits_{j} q(z_{j})\bigr) $$ both estimates are applied to $q(z|x)$...
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Do I need to train a separate DeepFake model for every input person?

I would like to create a deep fake model of a specific person (we will call him Steve). I would then like to be able to upload a video of any random person and swap their face with Steve's. So far I ...
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What's the difference between encoder/decoder and autoencoder? [duplicate]

What's the difference between the encoder-decoder model and autoencoder model? Or are they the same thing? Thanks!
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chatbot encoder/decoder: why do we need to use chatbot answer as the decoder inputs?

I am looking into the chatbot tutorial at: https://medium.com/predict/creating-a-chatbot-from-scratch-using-keras-and-tensorflow-59e8fc76be79 It uses sequence to sequence model with encoder/decoder ...
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Can I use/modify an Autoencoder to handle missing data?

I am about to implement an Autoencoder to detect anomalies. Therefore, e.g., in my test set, there is a situation where the data stream broke for some days. This results in a lack of data and should ...
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Results are too good.. what is wrong? How to predict correctly?

I am about to evaluate a neural network and want to check whether the predictions make sense. The variables: ...
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normalization/standardization of input/output of autoencoder and Gaussian Process

I have two machine learning algorithms that deal with time series data. My data consist of 1500 time series, each of 500 time components. The first machine learning algorithm is an autoencoder, ...
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Looking for a good text autoencoder model (for text reconstruction)

I am looking for a simple and good model that can learn to encode and reconstruct text sentences to use it in some downstream task. I tried a tensorflow seq2seq model here, but it doesn't do ...
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Removing layers from a convolutional encoding-decoding network

I've been reading this paper on Style-transfer (Universal Style Transfer via Feature Transforms): A crucial part of the algorithm (Section 3.3) uses a pre-trained VGG-19 network as an encoder to ...
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Validation generator in Autoencoder returning NaN

I am trying to build a fairly simple autoencoder using Keras on the OpenImages dataset. Here is the architecture of the ae: ...
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Keras - Autoencoder different from Encoder + Decoder

I build a CNN 1d Autoencoder in Keras, following the advice in this SO question, where Encoder and Decoder are separated. My goal is to re-use the decoder, once the Autoencoder has been trained. The ...
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VAE latent space dimension with Weibull PDFs as input data

I am currently using a Variational Autoencoder to reconstruct PDFs of Weibull distributions with varying parameters (sampled uniformly from a given parameter span). The PDFs are generated by sampling ...

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