All Questions
Tagged with neural-network autoencoder
84 questions
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Does Increasing Dimensionality Before Compression Make Sense for Anomaly Detection with Autoencoders?
Given a dataset $X$ of shape $(n, p)$ such that $n \gg 1$ and $p \approx 10$, I would like to train an autoencoder to solve an anomaly detection problem.
I did some experiments considering a classical ...
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Losing Information while resizing the image in Segmentation task using U-net
I'm using U-net architecture to build a segmentation task of image. During training I have image of size 256256 image. It works very well on the segmentation of same size 256256 or near to size 256*...
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740
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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.
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283
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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 ...
- 11
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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 ...
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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?
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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 ...
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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 ...
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How to detect anomalies?
I have timeseries data with one value per day for a year. (there is one column with temperature data). I am using autoencoders to train a reconstruction model with mse loss.
Firstly, I normalized the ...
- 13
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296
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Build autoencoder for single matrix with integer numbers
Can you please tell me how to build an autoencoder with a single matrix(4,4) with integer numbers?
I want to build an autoencoder for the below-mentioned data. I don't know whether I should convert ...
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92
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How should I think when I want to compare mu and sigma for different images in VAE?
I'm searching for a way to compare mu and sigma values of the encoder network's output of variational autoencoders.
In detail, imagine I trained my VAE on the MNIST digits dataset using the official ...
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What is the reason of this behavior of training loss in CONV auto-encoders?
I don't get Training Loss is steady up to the 7th epochs
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76
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Autoencoder not learning walk forward image transformation
I have a series of 15 frames with (60 rows x 50 columns). Over the course of those 15 frames, the moon moves from the top left to the bottom right.
Data = https://github.com/aiqc/AIQC/tree/main/...
- 549
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101
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time series anomaly detection
I want to ask for time series anomaly detection we can apply tnn on multiple features or not?
I used transformer for sentiment analysis where I have to provide a sentence and it predicts its output as ...
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Conditional variational autoencoder: Feeding labeled MNIST to encoder with Keras
I am looking for a code implementation of a CVAE using MNIST in Keras.
I found this Youtube video:
https://youtu.be/8wrLjnQ7EWQ
that does VAE, but I am not sure how do I convert this and make encoder ...
