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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How to choose the good number dimension of autoencoder?
I'm using Autoencoder for feature extracting. I stuck with how to choose good number of dimension of encoder layer (latent layer). After training dataset, the model gave the latent layer (embedding ...
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Anomaly Detection over multivariate data containing Nominal and numerical predictors
I am trying to implement Anomaly Detection over a multivariate dataset having nominal and numerical predictors.
Dataset has following pattern:
If we consider the below sample records, category_id, ...
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Does Anomaly Detection Algorithm works when the features are not correlated?
I am working on an Anomaly Detection Problem and the algorithm I used is an Autoencoder Multivariate Gaussian. The problem with my data is that it is unlabeled and not correlated.
For example, let's ...
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MNIST data shape
In going through the different tutorials on CNN, auto encoders and so on I trained my self on the MNIST problem. The different images are stored in a 3D array which shape is (60000,28,28). In some ...
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How to pass noisy images as input and original images as labels in Keras - Autoencoders
I want to make denoising autoencoder, I've added some noise to images, and i want to use them for training, original images will be used as label.
Reading the keras documentation I've found that I ...
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What's the best approach to classify labeled stockchart images , based on their curve
I'm working on an exercise where I have selected a lot of different stock chart images, showing the week closing of around 1200 companies at two different periode in time. I have stock charts from ...
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Information compression for variable input size
Is there a way to compress information of a variable input size?
Autoencoder requires standardized input sizes. Although I can add masks on the cost function and add dummy features to standardize ...
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Autoencoder neural network for dimensionality reduction of many-to-many regression problem
I want to find the relationship between a continues input-output using neural networks. The input consists of time series with more than 1000 data points. The output of each input time series is the ...
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questions on multi-step air pollution prediction
I am trying to use RNN to predict the concentration of various air pollutants for the next 24 hours.
The input data consists of 72 hours long and every hour owns 14 elements such as temperature, ...
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How to verify a CNN encoder works as expected?
I am using CNN as a part of kernel warping. The purpose here is to reduce input dimension (from N*M to K *1). The input data is not image data. I suspected that the CNN network might not work as I ...
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Identify the parameter causing the anomaly in a multivariate dataset
I have a payment transaction dataset with a large number of predictor variables. I am trying to build a model for anomaly detection and I have evaluated various algorithms/approaches for the same like ...
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Can I used a pre-trained auto encoder as an embedding layer in the model?
The dataset has an ordinal target variable [0,1,2]. Each observation in the dataset has multiple time series. In addition, each observation have various tabular features.
The purpose is to build a ...
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Derivation of ELBO in “An Introduction to Variational Autoencoders”
On page 18 of the paper titled "An Introduction to Variational Autoencoders" there is the following equation at the top of page 18. Paper can be found here.
$\log p_θ(\mathbf{x}) = \mathbb{E}_{q_{φ} (...
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VAE generates bad images. due to unbalanced loss functions?
I'm training a variational autoencoder on CelebA dataset using TensorFlow.keras
The problem I'm facing is that the generated images are not diverse enough and look kinda bad.
******(new) Example:****...
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Transformer-based architectures for regression tasks
As far as I've seen, transformer-based architectures are always trained with classification tasks (one-hot text tokens for example). Are you aware of any architectures using attention and solving ...
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1answer
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How to make custom callback in keras to generate sample image in VAE training?
I'm training a simple VAE model on 64*64 images and I would like to see the images generated after every epoch or every couple batches to see the progress.
when I train the model I wait until the ...
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Repeat in CNN autoencoder output
As I try to train a CNN autoencoder, the decoder tends to generate repeated patterns. These could be noises or poorly recognized input images. The period of these repeat increases with the number of ...
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Autoencoder to encode features/categories of data
My question is regarding the use of autoencoders (in PyTorch). I have a tabular dataset with a categorical feature that has 10 different categories. Names of these categories are quite different - ...
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Visualizing the value change in features in neural network
Lets say we have an autoencoder with three layers. And the data we feed to autoencoder is text data.
So in the middle layer, we will have a reduced dimension of the input data.
let's say 20 neurons in ...
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how many spectogram frames per input character does text-to-speech (TTS) system Tacotron-2 generate?
I've been reading on Tacotron-2, a text-to-speech system, that generates speech just-like humans (indistinguisahble from humans) using the github https://github.com/Rayhane-mamah/Tacotron-2.
I'm very ...
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Convolutional Autoencoders
I am writing a code for running autoencoder on CIFAR10 dataset and see the reconstructed images.
The requirement is to create
Encoder with First Layer
Input shape: (32,32,3)
Conv2D Layer with 64 ...
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13 views
How to determine the configuration of Conv2D layers?
I have this autoencoder with two parts:- encoder and decoder. However, I am having problem defining the configuration of these Conv2D layers. This is how my model look like:-
input_img = Input(shape=(...
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Graph disconnected between autoencoder's encoder and discriminator; why won't discriminator accept Input-layers as input?
I'm trying to create a semi-supervised Adversial Autoencoder model as described in section 5 of the AAE paper. The idea is for the discriminators to take in both the latent output from the encoder, as ...
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1answer
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How do I iterate over my images in dataset?
I am building an autoencoder with help from this site. There I was trying to build an autoencoder for my own custom data. My images are stored in a folder IMG and have names like ...
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34 views
Prefix Auto Encoder for Novelty Detection in-front of Image Classifier
I am working on a closed space(single subject) dataset that needs to be used for Image Classification. For eg., I just want the CNN to classify the model of the car.
I want a filter in-front of CNN ...
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CycleGAN vs. AutoEncoder for transforming sketches into images
I'm playing around with the use of deep learning on images and done quite works : colorizing black and white images for example, or maybe fixing old damaged photos.
Today I want to tackle a new ...
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Is there a standard dataset that I could use to test the implementation of a single layer autoencoder?
I am working with a dataset for regression.
I implemented an autoencoder with Keras, but when I try to reconstruct the data after dimensionality reduction, it does not reconstruct well.
Is there a ...
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Why is the 2 D latent space of the variational auto encoder getting constricted to a regression line
when I am training my variational autoencoder, the 2D latent space is getting constricted to a regression line instead of a Gaussian distribution.
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Ngram based Langauge Models learned using an Encoder-Decoder Model
I have been going through a Ngram based Langauge Model learned using an Encoder-Decoder Model for Email smart compose.
The program output only 1 prediction for given input.
I want to know how to ...
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42 views
What is the purpose of the minus sign in Adversarial Autoencoder
Iam currently implementing an Adversarial Autoencoder. Based on the architecture shown in the original paper I interpret that the input of the Discriminator is either the random sample from the true ...
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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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1answer
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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 ...
