Questions tagged [autoencoder]

Autoencoders are a type of neural network that learns a useful encoding for data in an unsupervised manner.

Filter by
Sorted by
Tagged with
1 vote
1 answer
46 views

Unsupervised Machine Translation System Using Variational Autoencoder Models

I want to work on an unsupervised machine translation system using a variational autoencoder. I did a literature review but didn't find any related work, and most of the work is based on denoising ...
kartikeya saraswat's user avatar
1 vote
1 answer
20 views

numpy in call method: how to run without eager execution?

I wrote an implementation of a feedback recurrent autoencoder in Keras. The key difference to a regular autoencoder is, that the decoded output is fed back to the input layers of both, encoder and ...
user155153's user avatar
0 votes
0 answers
33 views

How to work with multiple feature types on autoencoder?

This is my first post here. I am working on an adversarial autoencoder that receives different features, encodes them, and decodes them. For instance, suppose you have a dataset from a large survey ...
Umberto Mignozzetti's user avatar
0 votes
0 answers
42 views

How to detect rare events in Time series?

I am working on a time series dataset in which each time step can be classified under 4 classes: ~EOI : P(~EOI) = .85 ...
Fr_nkenstien's user avatar
0 votes
1 answer
11 views

Does the input data representation when training an autoencoder matter?

Suppose I want to train an autoencoder on English words. Does it matter what kind of input data representation I use? E.g., if I use word2vec as my input or bag of words, will the quality of my ...
a6623's user avatar
  • 101
0 votes
0 answers
37 views

Understanding the desired behavior of the loss function of Variational Autoencoders

So I understood that when training VAE, we need to weight the KL part of the loss with a weight less than 1 so that the reconstruction loss can get a chance to learn (avoiding the posterior collapse). ...
user1407562's user avatar
0 votes
0 answers
43 views

Modelling LSTM Autoencoder for anomaly detection with multiple Time Series

I'm currently working on LSTM autoencoder for anomaly detection. My main problem is I have multiple time series - each individual time series corresponds to a different customer, detailing their sales ...
suziex's user avatar
  • 1
0 votes
0 answers
9 views

Architectures for continuous vector labels prediction

I have a small dataset of feature vectors of size 200 to each corresponds a larger vector of size 1000. I would like to "predict" a large vector for every small one. The task sounds like ...
Denis Marcinkov's user avatar
1 vote
0 answers
27 views

Confusion over taking gradients in Variational Autoencoder (VAE)

I am confused as to when to hold certain parameters constant in a VAE. I will explain with a concrete example. We can write $\operatorname{ELBO}(\phi, \theta) = \mathbb{E}_{q_{\phi}(z)}\left[\log \...
Joel's user avatar
  • 11
0 votes
1 answer
73 views

How does a VQ-VAE produce new images?

I'm implementing a VQ-VAE for a LDM for biological time series data. I trained the VQ-VAE, and reconstructions works somewhat reasonable, but I have an understanding problem with how a VQ-VAE works. ...
Jackilion's user avatar
0 votes
1 answer
73 views

Constructing an LSTM autoencoder for variable-lentgth sequences

I would like to construct an LSTM autoencoder model for sequence anomaly detection where the sequences can be varying in length. I understand based on this answer that padding and masking can be used ...
interoception's user avatar
0 votes
0 answers
69 views

Seq2seq Transformer Autoencoder returns same results when unfolded using only memory and initial value

I have numeric signals from two sensors, and I would like to create a mapping using sequence-to-sequence autoencoder. I used the transformer architecture, and it seems to be learning - the loss is ...
David Harar's user avatar
0 votes
0 answers
16 views

Binary latent representation

I've been working on a problem where I got stuck at encoding my data into a binary latent representation, most of the methods out there aren't really working for my case. I have input for the encoder ...
HAMDI ABDERRAHMENE's user avatar
0 votes
0 answers
22 views

Using Autoencoder in Python for Data Selection and Risk Level Calculation

I have a dataset that includes 100 data points from each sensor, representing various measurements. These measurements can be used to calculate the level of risk associated with each sensor. However, ...
JACK1's user avatar
  • 1
3 votes
2 answers
193 views

How does variational autoencoders actually work in comparison to GAN?

I want to know about how variational autoencoders work. I am currently working in a company and we want to incorporate variational autoencoders for creating synthetic data. I have questions regarding ...
NevMthw's user avatar
  • 47
0 votes
0 answers
16 views

Multiagent observations encoding - LSTM or Transformer?

In multi agent learning problems where agents can join or leave, to address the issue of varying observation space I am trying to build a latent representation either using LSTM or transformer which ...
SathukaBootham's user avatar
0 votes
0 answers
13 views

How can I ask a conditional auto encoder to put more emphasis on the condition without setting epochs too high?

Here's an example of what I'm testing on. I have the mnist digits data and I randomly add a random number from 1 to 10 to each digit and divide it by 11. I do this because I wanted to test out the ...
purple1437's user avatar
0 votes
0 answers
15 views

Counting Strikes from Dynamic Probing: Reliable Impulse Detection

Im looking for general guidance and advice to solve a specific application problem. Let me explain the contect briefly: In geotechnics, there is a method called "dynamic probing" to examine ...
northwind887's user avatar
0 votes
0 answers
21 views

LSTM Autoencoder Failing to Capture Amplitudes of Input Signals

I'm trying to create an LSTM autoencoder to encode some high frequency time series data. The data has 206 time steps and I'm trying to both encode this down to a lower dimension and also recreate the ...
Bic's user avatar
  • 1
0 votes
0 answers
36 views

Minimizing the difference between two distributions with TensorFlow

Suppose that the encoding neural network of a variational autoencoder (VAE) outputs a distribution from which latent samples will be drawn. To do this, the layer ...
Value_Investor's user avatar
0 votes
0 answers
183 views

Transformer-based autoencoder generates same output and bad embeddings

I am trying to implement the transformer-based autoencoder presented in this paper: https://arxiv.org/abs/2210.08288 The paper seems rather vague to me and I do not fully understand how the model is ...
Droidenkiller's user avatar
0 votes
0 answers
188 views

Incompatible Shape Error [32, 150,150,3] vs [32] for an Autoencoder

Please Help, I am trying to make an Autoencoder from a dataset of 533,248 images but I can't seem to get the shape right when I go to fit my model. The code I have is below. ...
Ryan Geisen's user avatar
0 votes
0 answers
26 views

Pixelated output of an autoencoder network

I have an autoencder that encodes an input size of (76, 400, 1) in a 2D convolutional layer, and decodes it an output size of (125, 400, 1). Both downsampling and upsampling are performed using a '...
BaltiNalti's user avatar
0 votes
1 answer
86 views

Deepstack autoencoder

I'm trying to go through the first edition tabular challenge on Kaggle. Obviously my first few trials results did not satisfy me, so I went to see how other people did, and in the post of the first ...
Marek M.'s user avatar
1 vote
0 answers
18 views

Generating artificial training data with encoder and classical algorithm

I would like to know if this idea has been tried before, and if so, where I can find more information about it. This is an approach to generating artificial training data for segmentation tasks using ...
dave4422's user avatar
0 votes
0 answers
51 views

autoencoder feature extraction

I'm very new to deep learning and come across an idea of feature extraction by using autoencoder. I went through many example online and came across these following. https://www.thepythoncode.com/...
Saetthakij Naothaworn's user avatar
0 votes
0 answers
13 views

Larger Latent Space Dim for Point Cloud Autoencoder

So I'm trying to follow a paper that uses a AE to learn point clouds. The thing is, the dimension of the point cloud data is 3 (x, y, z), but the dimension of the latent space from what I can tell is ...
okayatcp12's user avatar
1 vote
0 answers
64 views

Threshold for auto encoder anomaly detection

I have fitted an auto encoder on 25-dimensional time series data hoping to be able to detect anomalies. training set is 100k observations, testset for threshold setting is 10k observations. all ...
Student's user avatar
  • 21
0 votes
0 answers
28 views

Cluster-Based Anomaly Detection (and PCA)

I have a dataset of user operations (250 types of operations) in a trading platform and my task is to extract features, flagging rules, other insight for fraud prevention/anomaly detection, or some ...
mtcicero's user avatar
0 votes
1 answer
17 views

How can I show data clustering after building a Convolutional Variational Autoencoder?

I am brand new to Machine Learning, and just followed Tensorflow Convolutional Variational Autoencoder Tutorial using my own dataset instead of the MNIST set. I was able to successfully display the 2D ...
android_developer's user avatar
2 votes
1 answer
182 views

Early anomaly detection / Failure prediction on time series

My problem here is that I want to predict failures in advance with respect to their occurrence. I have sensors mounted on my machine and with a certain frequency, they send data to my database. ...
Andrea's user avatar
  • 45
0 votes
1 answer
212 views

What is the dataset during testing a Variational auto-encoder?

I am getting confused in the testing dataset of a VAE. After training the VAE, what should be the testing data-set of the VAE? I understand that during testing the VAE only has the decoder part. Hence,...
Formal_this's user avatar
0 votes
0 answers
48 views

Measuring the "smoothness" of the latent space of a (variational) autoencoder

Suppose I have an autoencoder (or variational autoencoder) that encodes language. I want to compare different training regimes and evaluate the AE / VAE on how "smooth" its latent space is. ...
postnubilaphoebus's user avatar
1 vote
1 answer
117 views

Autoencoder: How should hidden layer be used?

I'm building a variational autoencoder to generate faces. I'm using gray-scale images with the size 30x30. I started with a very simple model: Input Layer, 900 nodes, values 0-1 Latent Space, 10 nodes ...
lev1248's user avatar
  • 11
0 votes
0 answers
32 views

Feature extraction using autoencoder produces latent vector with large elements

Good day. I am experimenting with a convolution autoencoder, represented below, for feature extraction. The latent vectors have large numbers, > 10 and < -10, that cause problems for later ...
Tom Carroll's user avatar
1 vote
0 answers
29 views

Python train convolutional on numerical values shape issue

I want to train a convolutional neural network autoencoder on a csv file which contains values pixel neighborhood position of an original image of 1024x1024. When I try to train it, I have the ...
user979974's user avatar
1 vote
1 answer
54 views

Why VAE Encoder outputs log variance and not standard deviation?

When talking about VAE (and viewing VAE implementations), the Encoder outputs: μ, log(variance) when we train the model (the ...
user3668129's user avatar
0 votes
1 answer
170 views

Tips for making an autoencoder converge?

I am currently trying to make an autoencoder that compresses a 3D volume where each value represents the density of said volume. The architecture is a UNet without skip connections. The optimizer is ...
truvaking's user avatar
  • 101
0 votes
1 answer
45 views

How to train encoder in BiGAN?

I have some difficulties training a BiGAN. In particular, the encoder seems not learning the map between the images x and the latent space z. I have the following encoder: ...
Pippo's user avatar
  • 1
0 votes
0 answers
68 views

Is it possible to use text Auto Encoders without text generation?

I have a use case where I have large texts, and a lot of it. Pretty often the sequence length exceeds 1000 tokens. I need a lower dimensional compression of the texts as an input for a classifier. The ...
thijsvdp's user avatar
  • 101
0 votes
2 answers
367 views

Autoencoder vs Pre-trained network for feature extraction

I wanted to know if anyone has any sort of guidance on what is better for image classification on a lot of classes (about 400) with a small amount of samples per class (around 20), for relatively big ...
MrStealYourFrog's user avatar
0 votes
0 answers
54 views

Practical application of denoising autoencoders

I have been reading into autoencoders for the purpose of denoising data. In the examples i found (eg. [1, 2, 3], which are the first few google results) they have the following input/output: Input ...
Leander Moesinger's user avatar
0 votes
1 answer
69 views

Questions on GLM: General Language Model Pretraining with Autoregressive Blank Infilling

For GLM: General Language Model Pretraining with Autoregressive Blank Infilling , May I ask how is the sampling for input division in step (b) being done ? why in step (c), the green ...
kevin's user avatar
  • 283
0 votes
1 answer
20 views

What says the output of autoencoder?

What is the meaning of output of autuencoders? Can we say it is the noise removed version of actual dataset and should it be symmetrical?
MobiusT's user avatar
  • 11
1 vote
0 answers
43 views

How can I use autoencoders for noise detection and removal

How can I use autoencoders for noise detection and removal in a dataset with only 2 features and no labels? How should my architecture be like, such as 2 1 1 1 2 or any other? And does the output of ...
MobiusT's user avatar
  • 11
0 votes
1 answer
64 views

Trimming "unused" neurons from the bottleneck of an autoencoder

I'm working with autoencoding data in segments, and working with the latent space afterwards (I am also working on VAEs, but this segment of the project concerns deterministic AEs). I've noticed that ...
Whitehot's user avatar
  • 101
1 vote
0 answers
92 views

How to implement simple VAE with sparse tensor in Tensorflow

thanks for reading. I have been attempting to train a simple VAE on very sparse 2D and 3D data. So far I have been training using dense tensors which - I think - is resulting in horrible training due ...
Zephrom's user avatar
  • 11
1 vote
0 answers
40 views

Training a straight "copy the input to the output" autoencoder for audio is strangely slow

As a learning exercise, I'm training a "perfect" audio autoencoder. It has a hidden layer just as wide as the input layer, with linear activation. The expectation is that the network should ...
Alexander Ljungberg's user avatar
1 vote
0 answers
28 views

Deep learning model to predict the actual input values

I have some observed parameters to be used as input to the deep learning model. This problem comes from the wireless field where we transmit $x$. The channel $h$ is random in nature. The received ...
Riva11's user avatar
  • 11
0 votes
1 answer
53 views

Autoencoder general questions and poor loss

I'm trying to get a simple autoencoder working on the iris dataset to explore autoencoders at a basic level. I'm running into an issue where the loss of the model is extremely high (>20). Can ...
user37649's user avatar

1
2 3 4 5
7