Questions tagged [autoencoders]
For questions about autoencoders, a type of unsupervised artificial network for learning efficient data codings.
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How much data do we need for making a successful de-noising auto-encoder?
Is there a guide how much data do you need for making successful denoising model using autoencoders?
Or the rule is, the more data, the better it is?
I tried with small dataset 350 samples, to see ...
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2answers
21 views
How can I have the same input and output shape in an auto-encoder?
I'm building a denoising autoencoder. I want to have the same input and output shape image.
This is my architecture:
...
2
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1answer
29 views
What are the main differences between sparse autoencoders and convolution autoencoders?
What are the main differences and similarities between sparse autoencoders and convolution autoencoders?
When should one be preferred over the other? What are their applications?
(References are ...
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31 views
Does the reduction of the dimensions over multiple layers allow more details to be stored within the final representation?
From : https://debuggercafe.com/implementing-deep-autoencoder-in-pytorch/
the following autoencoder is defined
...
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25 views
How estimate the minimum size of an autoencoder to overfit the training data?
Given e.g. $1$M vectors of $1000$ floating points each, where every point in vectors is sampled from a uniform distribution between $-1$ to $1$, how to estimate the minimum network size required ...
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1answer
33 views
Is it possible to have the latent vector of an auto-encoder with size 1?
Given e.g. 1M vectors of $1000$ floating points each, where every point in vectors is sampled from a uniform distribution between $-1$ to $1$:
Is it possible to have the bottleneck of the AE network ...
3
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1answer
29 views
How to evaluate the performance of an autoencoder trained on image data?
I am training an autoencoder on (general) image data.
I use binary crossentropy loss function, but it is not very informative when I want to evaluate the performance of my autoencoder.
An obvious ...
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0answers
26 views
How to convert something to vectors
I wanted to create an encoder, which is the first part of an autoencoder. I do not want to build the whole autoencoder but rather wanted to test whether my mobile device can support running an encoder ...
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1answer
26 views
How fast are autoencoders?
I was exploring image/video compression using Machine Learning. In there I discovered that autoencoders are used very frequently for this sort of thing. So I wanted to enquire:-
How fast are ...
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1answer
29 views
Can I use an autoencoder with high latent representational space?
I am trying to use a neural network to predict the next state output given the current state and action pairs. Both input and outputs are continuous variables. Due to the high dimensionality of each ...
1
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1answer
47 views
Can a variational auto-encoder learn images composed of random noise at each pixel (each drawn from the same distribution)?
Can a variational auto-encoder (VAE) learn images whose pixels have been generated from a Gaussian distribution (e.g. $N(0, 1)$), i.e. each pixel is a sample from $N(0, 1)$?
My gut feeling says no, ...
2
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0answers
54 views
Adding a dense layer after a conv2d layer in a convolutional autoencoder
I am trying to implement a convolutional autoencoder with a dense layer at the bottleneck do to some dimensional reduction. I have seen two approaches for this which arent particularly scalable. The ...
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0answers
17 views
Can denoising auto-encoders be convolutional and fully connected?
I have been reading lately on autoencoders a lot. I just wanted to summarize my understanding of denoising autoencoders. As far as I understand they can be
Fully connected (in which case, they will ...
1
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1answer
33 views
How can I use autoencoders to analyze patterns and classify them?
I generated a bunch of simulation data from a complex physical simulation that spits out patterns. I am trying to apply unsupervised learning to analyze the patterns and ideally classify them into ...
1
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1answer
29 views
How to get top 5 movies recommendations from Auto-Encoder
I have trained a model using Auto-encoder on movielens dataset. Below is how i trained the model.
...
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1answer
31 views
Why is exp used in encoder of VAE instead of using the value of standard deviation alone?
There's one VAE example here:
https://towardsdatascience.com/teaching-a-variational-autoencoder-vae-to-draw-mnist-characters-978675c95776.
And the source code of encoder can be found at the ...
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1answer
35 views
What is the mean in the variational auto-encoder?
Here's a diagram of a variational auto-encoder.
There are 2 nodes before the sample (encoding vector). One is the mean, one is the standard deviation. The mean one is confusing.
Is it the mean of ...
2
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1answer
40 views
Autoencoder network for feature selection not converging
I am training an undercomplete autoencoder network for feature selection. I am using one hidden layer in the encoder and decoder networks each. The ELU activation function is used for each layer. For ...
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1answer
37 views
What class of problem is this?
If I have a lot of input output pairs as training data
<float Xi, float Yi>
and I have a parametrized approximation function (I know the function algorithm, ...
4
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1answer
52 views
Autoencoder produces repeated artifacts after convergence
As experiment, I have tried using an autoencoder to encode height data from the alps, however the decoded image is very pixellated after training for several hours as show in the image below. This ...
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1answer
37 views
How are small scale features represented in an Inverse Graphics Network (autoencoder)?
This post refers to Fig. 1 of a paper by Microsoft on their Deep Convolutional Inverse Graphics Network:
https://www.microsoft.com/en-us/research/wp-content/uploads/2016/11/kwkt_nips2015.pdf
Having ...
2
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0answers
73 views
Is it possible to use entity embedding with autoencoder for anomaly detetction?
I'm trying to build autoencoder in keras in order to detect anomalies. However, most of the data is categorical and I have to encode it. When it comes to production, categorical features can take new ...
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0answers
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Why is my variational auto-encoder generating random noise?
This is my first variational autoencoder. Background info: I am using the MNIST digits dataset. The model is created and trained in PyTorch. The model is able to get a reasonably low loss, but the ...
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1answer
87 views
What are some new deep learning models for learning latent representation of data?
I know that autoencoders are one type of deep neural networks that can learn the latent representation of data. I guess there should be several other models like autoencoders.
What are some new deep ...
2
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1answer
43 views
Why does the denoising autoencoder always returns the same output?
I am trying to implement a denoising autoencoder (DAE) to remove noise from 1024-point FFT spectra. I am using two types of spectra: (1) that contain a distinctive high amplitude spectral peak and (2) ...
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1answer
66 views
Concrete example of latent variables and observables plugged into the Bayes' rule
In the context of the variational auto-encoder, can someone give me a concrete example of the application of the Bayes' rule
$$p_{\theta}(z|x)=\frac{p_{\theta}(x|z)p(z)}{p(x)}$$
for a given latent ...
2
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0answers
81 views
How does deepfake technology work with multiple people in a single frame?
I was watching this video from corridor crew, according to them, they have used deepfake technology to create this video. I myself have never made a deepfake videos, but I have enough knowledge in the ...
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0answers
27 views
Reduce same sample distance in VAE encodings
I'm working on a beta VAE model learning a latent representation used as a similarity metric for image registration.
One of the main problems I'm facing is that the encoder + sampler output doesn't ...
2
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0answers
31 views
Are there any general tips for troubleshooting a VAE when apparently it is not learning?
I am trying to train a VAE for anomaly detection. I chose one architecture from this Github repository and I adjusted the input and output to match what I need. In my case, the input (and hence the ...
2
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1answer
48 views
Using ML to encypher data for production
I am looking for research and experience working with ML models to ingest data for tasks, like text analysis, and creates a system that copies (or in other words enciphers) the input data, to then ...
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1answer
57 views
What can I do with an autoencoder? [duplicate]
I cannot find information in detail about autoencoder
What can I do with an autoencoder (and how can I do this), practically speaking?
What does the encoder (this part I think I understand) and a ...
3
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2answers
41 views
Dealing with empty frames in MRI images
I started working on the application of deep learning in medical imaging recently. While dealing with MRI images in the BraTS dataset, I observe that first and last few frames are always completely ...
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0answers
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Camera pose to environment Mapping
I would like to teach a model the environment of a room. I'm doing so by mapping a camera pose (x, y, z, q0, q1, q2, q3) to its corresponding image; where x, y, z represent location in Cartesian ...
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1answer
208 views
Is there a Continuous Conditional Variational Autoencoder?
The Conditional Variational Autoencoder (CVAE), introduced in the paper Learning Structured Output Representation using Deep Conditional Generative Models (2015), is an extension of Variational ...
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1answer
70 views
What is the best loss function for convolution neural network and autoencoder?
What is the best choice for loss function in Convolution Neural Network and in Autoencoder in particular - and why?
I understand that the MSE is probably not the best choice, because little ...
2
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1answer
59 views
How should I detect an object in a camera image?
I would like to create a model, that will tell me if one type of object is in an image or not.
So, for example, I have a camera and I would like to see when one object gets into the shot.
Object ...
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0answers
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Limits for a bottleneck
I have some 64x64 pixels frames from a (simulated) video, with a spaceship moving on a fixed background. The spaceship moves in a straight line with constant velocity from left to right (along the x-...
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2answers
411 views
Autoencoder for MobileNetV2
I have way more unlabeled data than labeled data. Therefore I would like to train an Autoencoder using MobileNetV2 as the encoder. Then I will use the pretrained model for the classification of the ...
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0answers
27 views
Deep Generative Networks Probability of “Success”
I have built various "successful" GANs or VAEs that can generate realistic images reliably, but in either case the generative step is sampling a latent feature vector from some distribution and ...
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0answers
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How to learn to sample?
Imagine you have access to a dataset of pairs $(s, \hat{\pi}(s))$ where s is a state in a high dimension continuous space $S$, $\pi(s)$ is a probabilistic distribution on a large discrete space $D$ (...
2
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1answer
76 views
Reconstruction Errors in Auto Encoders after Training
Autoencoders are used for unsupervised anomaly detection by at first learning the features of the data set with mainly "normal" data points. Then new data can be considered anomalous, if the new data ...
2
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1answer
52 views
How to add some data input in a CNN?
There is this problem I have encountered, I was trying to classify the pixels from input image into classes, sort of like segmentation, using a encoder-decoder CNN. The “interested” pixels usually ...
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2answers
193 views
Why are VAE's useful?
I am not sure I understand what is the advantage of using a VAE's over a deterministic Auto Encoder? For example, assuming we have just 2 labels, a deterministic Auto Encoder will always map a given ...
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0answers
77 views
Can we use Autoencoders for unsupervised CNN feature learning?
I searched through the internet but couldn't find a reliable article that answers this question.
Can we use Autoencoders for unsupervised CNN feature learning of unlabeled images like the below
and ...
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1answer
236 views
Autoencoder for color images in Keras backed by MXNet
I need to train an autoencoder in Keras with the JPG images I took myself.
...
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1answer
585 views
Variational Autoencoder task for better feature extraction
I have a CNN with the regression task of a single scalar.
I was wondering if an additional task of reconstructing the image (used for learning visual concepts), seen in a DeepMind presentation with ...
3
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2answers
105 views
Do we also need to model a probability distribution for the decoder of a VAE?
I'm working on understanding VAEs, mostly through video lectures of Stanford cs231n, in particular lecture 13 tackles on this topic and I think I have a good theoretical grasp.
However, when looking ...
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4answers
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What are the purposes of autoencoders?
Autoencoders are neural networks that learn a compressed representation of the input in order to later reconstruct it, so they can be used for dimensionality reduction. They are composed of an encoder ...
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1answer
406 views
How should we choose the dimensions of the encoding layer in auto-encoders?
How should we choose the dimensions of the encoding layer in auto-encoders?
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1answer
106 views
Why isn't the Credit Card Fraud Detection dataset from Kaggle already balanced?
I am working on a credit card fraud detection problem using autoencoders. I have a question regarding the dataset I'll be using.
I've downloaded the dataset for the above problem from Kaggle, which ...
