Questions tagged [autoencoders]
For questions about autoencoders, a type of unsupervised artificial network for learning efficient data codings.
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How to quantify the amount of information lost by the decoder NN in an AE?
Is there a way to quantify the amount of information lost in the lossy part of an autoencoder where the original input is compressed to a representation with less degrees of freedom?
I was thinking ...
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19 views
What is the time complexity for training a single-hidden layer auto-encoder?
What is the time complexity for training a single-hidden layer auto-encoder, for 1 epoch?
You can assume that there are $n$ training examples, $m$ features, and $k$ neurons in the hidden layer, and ...
2
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0answers
29 views
Enforcing sparsity constraints that make use of spatial contiguity
I have a deep learning network that outputs grayscale image reconstructions. In addition to good reconstruction performance (measured through mean squared error or some other measure like psnr), I ...
4
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0answers
24 views
Are there transformer-based architectures that can produce fixed-length vector encodings given arbitrary-length text documents?
BERT encodes a piece of text such that each token (usually words) in the input text map to a vector in the encoding of the text. However, this makes the length of the encoding vary as a function of ...
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1answer
22 views
Which type of feature extractor do you suggest to classify sensor data?
I have IMU (Inertial Measurment Unit- 6 axis) sensor data. The sensor attached on a car and 7 different drivers wipe on same path. I want to extract features and classify drivers. Which type of ...
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2answers
96 views
Why don't we use auto-encoders instead of GANs?
I have watched Stanford's lectures about artificial intelligence, I currently have one question: why don't we use autoencoders instead of GANs?
Basically, what GAN does is it receives a random vector ...
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22 views
What is the best activation function for the embedding layer in a deep auto-encoder?
I am designing a deep autoencoder for graph embedding (exactly node embedding) following this paper SDNE. In the original paper, they used the sigmoid activation for all hidden layers in the ...
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1answer
34 views
How to determine the number of hidden layers and units of deep autoencoder?
I am using a deep autoencoder for my problem. However, the way I choose the number of hidden layers and hidden units in a hidden layer is still based on my feeling.
The size of the model that ...
3
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0answers
52 views
Looking for the proper algorithm to compress many lowres images of nearby locations
I have an optimization problem that I'm looking for the right algorithm to solve.
What I have: A large set of low-res 360 images that were taken on a regular grid within a certain area. each of these ...
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0answers
74 views
How can a de-noising auto-encoder act as an anomaly detection model?
In some research papers, I have seen that, for training the autoencoders, instead of giving the non-anomalous input images, they add some anomalies to the normal input images, and train the auto-...
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1answer
64 views
What should the output of a neural network that needs to classify in an unsupervised fashion XOR data be?
XOR data, without labels:
[[0,0],[0,1],[1,0],[1,1]]
I'm using this network for auto-classifying XOR data:
...
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26 views
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
56 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
38 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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42 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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35 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
43 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
51 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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33 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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2answers
67 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
36 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 ...
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1answer
58 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, ...
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0answers
143 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
24 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 ...
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1answer
41 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 ...
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1answer
32 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
45 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 ...
2
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1answer
41 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
45 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
40 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
71 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
40 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
83 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
39 views
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 ...
4
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1answer
93 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
64 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) ...
5
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1answer
69 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
126 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
28 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
40 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
50 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
60 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
42 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 ...
4
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1answer
239 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
79 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
62 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
17 views
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
509 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
28 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 ...
