Questions tagged [autoencoders]
For questions about autoencoders, a type of unsupervised artificial network for learning efficient data codings.
112
questions
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13 views
Why is the prior on the latent variable standard gaussian in VAE?
While training a standard VAE, we assume that the prior on the latent variable Z is the standard gaussian and we use KL divergence to push the posterior as close as possible to the standard gaussian. ...
1
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1answer
29 views
Are mean and standard deviation in variational autoencoders unique?
In general, if I have a collection of data then mean(Expectation) and standard deviation are calculated as follows
$$\text{mean } = \mu = \mathbb{E}[X] = \sum\limits_{i = 1}^n p_ix_i $$
$$\text{...
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0answers
12 views
Train separate AutoEncoder's on each class or one AE for all classes to learn features?
I'm working on a project where the dataset contains time series of three classes, depending on the shape of the series. I want to learn the representations of these series as vectors, so naturally I ...
1
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0answers
20 views
What is the conceptual difference between convolutional neural networks and auto-encoders?
I'm familiar with Auto-Encoders and I'm about to dive into CNNs. By having a look at the most important component of a CNN, the filter:
I wonder how it is different from Auto-Encoders:
For me, it ...
0
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0answers
67 views
Can I use the VAE for dimensionality reduction?
I'm doing a project that uses a clustering algorithm for the facial expression classification task. So, I use the output of the encoder in the VAE autoencoder for dimensionality reduction. However, I ...
0
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0answers
23 views
Problems while transforming a 2D Variational Autoencoder into a 1D Version
I am trying to addapt the Keras variational autoencoder (VAE) here from a 2-D input/output (matrix of a picture) to a 1-D input/output (just a vector). I thought this would be a fearly easy task, but ...
0
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0answers
27 views
Weird KL divergence behaviour
I'm training a complex model for motion prediction using a VAE, however the KL divergence has a very strange behavior.
A scheleton of the network is the following:
At the end my network compute the ...
0
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0answers
31 views
Why would an auto-encoder produce latent vectors with many zeros?
My autoencoder give latent vectors with many zeroes components like:
...
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0answers
27 views
How do I select the number of neurons for each layer in an auto-encoder for dimensionality reduction?
I am trying to apply an auto-encoder for dimensionality reduction. I wonder how it will be applied on a large dataset.
I have tried this code below. I have total of 8 features in my data and I want to ...
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1answer
27 views
error while fitting auto encoder for anomaly detection on sensor data [closed]
I have sensor data like this and it's unsupervised and it has almost 800 features. my use-case is for anomaly detection. I applied Principal component analysis (PCA) for dimensionality reduction. now ...
0
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1answer
24 views
How to train a model for 1 image class to detect anomaly?
I want to train a model with python over the images, and these images are for a metal product.
my aim is to detect the defects, to notice if a product is a failure.
what kind of architecture do you ...
0
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2answers
96 views
In variational autoencoders, why do people use MSE for the loss?
In VAEs, we try to maximize the ELBO $\mathbb(E_q log\ p(x|z) + D_{KL}(q(z|x), p(z))$), but I see that many implement the first term as MSE of the image and it's reconstruction. Is this mathematically ...
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0answers
29 views
Autoencoder on Sharp Images
I am training an autoencoder to reconstruct 3D images. This is going quite well apart from one slight issue.
The images I wish to reconstruct are binary representations of organs. This means that they ...
0
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0answers
115 views
What is the difference between the forward pass of the Multi-Layer Perceptron, Deep AutoEncoder and Deep Belief Network?
Multi-Layer Perceptron (MLP), Deep AutoEncoder (DAE), and Deep Belief Network (DBN) are trained differently.
However, do they follow the same process during the inference phase, i.e., do they ...
0
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1answer
38 views
Convert LSTM univariate Autoencoder to multivariate Autoencoder
I have the following code snippet which takes in a single column of value i.e. 1 feature. How do I modify the LSTM model such that it accepts 3 features?
...
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0answers
24 views
Compression of real time speech data
I am trying to train 2 neural networks - one to compress and the other to decompress real time speech data, and I have a few questions.
First off
Would this even work - trained, of course, on the same ...
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0answers
16 views
Can an Autoencoder be used to create a simple text-completion plugin (code-completion for programming)?
Can an Autoencoder neural network be used to create a simple code completion plugin for a developer using a certain programming language ?
The idea is that the training data will be generated from ...
0
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1answer
42 views
Why Autoencoder Weights Are Not Always Tied
To me, tying weights in an autoencoder makes sense if we think of the auto encoder as doing PCA. Why in any situation would it make sense to not tie the weights? If we don't tie the weights, would it ...
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0answers
19 views
Dealing with bias in multi-channel auto encoders
The problem
I have a multi-channel 1D signal I want to auto-encode.
I am unable to resonstruct the input when the number of channels increases.
Code
I am using a convolutional encoder, and a ...
1
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0answers
22 views
Underfitting a single batch: Can't cause autoencoder to overfit multi-sample batches of 1d data. How to debug?
TL;DR
I am unable to overfit batches with multiple samples using autoencoder.
Fully connected decoder seems to handle more samples per batch than conv decoder, but then also fails when number of ...
2
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1answer
71 views
How does replacing states with latent representations help RL agents?
I have seen many papers using autoencoders to replace images (states) with latent representations. Some of those methods have shown higher rewards using such techniques. However, I do not understand ...
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0answers
33 views
variational auto encoder loss goes down but does not reconstruct input. out of debugging ideas
My variational autoencoder seems to work for MNIST, but fails on slightly "harder" data.
By "fails" I mean there are at least two apparent problems:
Very poor reconstruction, for ...
1
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0answers
70 views
VAE giving near zero output when latent space dimension is large
I'm training a VAE to reconstruct some input (channels picked up by some MIMO BS for context) and I ran an experiment on the training set to see how the performance improves with the latent space ...
0
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0answers
36 views
Is the char-CNN-RNN text encoder is an encoder part of an auto-encoder?
The char-CNN-RNN encoder is relatively a popular encoder. It was proposed in the paper titled Learning Deep Representations of Fine-Grained Visual Descriptions by ...
0
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0answers
66 views
How to compare multiple one-class variational autoencoders?
I have trained multiple one-class vanilla variational autoencoders that each learn the distribution of one class and have the same architecture. The classes are mostly discrete, but there are several ...
2
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0answers
30 views
Literature on the advantages of using an auto-encoder for classification
Given a supervised problem with X, y input pairs, one can do two things for obtaining the function f that maps X with y with Neural Networks (and in general in machine learning):
Deploy directly a ...
1
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1answer
69 views
Autoencoder: predictions missing for nodes in the bottleneck layer
I'm using tf.Keras to build a deep-fully connected autoencoder. My input dataset is a dataframe with shape (19947,), and the purpose of the autoencoder is to predict normalized gene expression values. ...
2
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0answers
27 views
Compressing Parameters of an Response System
I have an input-output system, which is fully determined by 256 parameters, of which I know a significant amount are of less importance to the input-output pattern.
The data I have is some (64k in ...
0
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0answers
36 views
Which models can I use for supervised learning with images?
I have to do a project that detects fabric surface errors and I will use machine learning methods to deal with it. I have a dataset that includes around six thousand fabric surface images with the ...
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0answers
28 views
How can I improve the performance on unseen data for semantic segmentation using an auto-encoder?
I am using simple autoencoders for the task of semantic segmentation on the VOC2012 dataset. I am currently using a simple autoencoder based model. It is trained on adam optimizer with cross-entropy ...
1
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0answers
22 views
Why do we add additional axis in CNN autoencoder while denoising?
I am currently learning about autoencoders and I follow https://www.tensorflow.org/tutorials/generative/autoencoder
When denoising images, authors of tutorial add an additional axis to the data and I ...
2
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0answers
33 views
how to handle highly imbalanced multilabel classification?
I am working on a multilabel classification in which I am having 206 labels. When I saw the percentage of the number of 1's in each label they are way less than 0.1% for each label. The maximum ...
1
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2answers
174 views
Is it possible to have a variable-length latent vector in an autoencoder?
I'm trying to have a simple autoencoder but with variable latent length (the network can produce variable latent lengths with respect to the complexity of the input), but I've not seen any related ...
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0answers
30 views
How did they use their dataset with VAEs?
Old Photo Restoration via Deep Latent Space Translation (https://paperswithcode.com/paper/old-photo-restoration-via-deep-latent-space)
In the article, it says : "We propose to restore old photos ...
1
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1answer
84 views
Why does the training time of SVMs dramatically decrease after applying dimensionality reduction to the features?
Training an SVM with an RBF kernel model with c = 5.5 and gamma = 1.06, for a 5-class classification problem on the NSL-KDD ...
1
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0answers
41 views
Role of autoencoder in Hierarchical Extreme Learning Machine
I want to build HELM neural network that consists of autoencoder (AE) and one class classification (OC).
HELM with AE and OC have following shape:
That is, hidden layer output of AE is input of OC.
...
0
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0answers
45 views
Deep Continuous Clustering algorithm - just one output cluster
I use the DCC algorithm to cluster some data.
The whole algorithm is available here, but shortly it is:
construct mkNN graph of the data points (the connected components of it are the clusters).
...
2
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1answer
65 views
Are Autoencoders for noise-reduction only suited to deal with salt-and-pepper kind of noise?
I'm currently looking at NN to deal with noisy data. I like the Autoencoder approach https://medium.com/@aliaksei.mikhailiuk/unsupervised-learning-for-data-interpolation-e259cf5dc957 because it seems ...
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0answers
24 views
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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0answers
124 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 ...
3
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0answers
37 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 ...
5
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0answers
54 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 ...
0
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1answer
25 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 ...
6
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2answers
159 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 ...
1
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1answer
437 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 ...
1
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1answer
144 views
How to determine the number of hidden layers and units of a deep auto-encoder?
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
53 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 ...
1
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0answers
77 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-...
1
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1answer
76 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:
...
1
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0answers
31 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 ...
