Questions tagged [autoencoder]

Question 1

I am training a VAE architecture on microscopy images. Dataset of 1000 training images, 253 testing images. Images are resized to 128x128 input or 256x256 input from original resolution which is ...

Question 2

I might be mistaken, but based on my current understanding, autoencoders typically consist of two components: encoder $f_{\theta}(x) = z$ decoder $g_\phi(z)=\hat{x}$ The goal during training is to ...

Question 3

Im trying to predict pregnancy codes with a basic transformer model architecture. These pregnancy codes are like following prg001, prg002 to prg030. Prg001 would be antenatal screening and prg030 ...

Question 4

I am trying build an autoencoder which would encode the image into latent space and then decode it back to the original image without any changes. I am mainly trying to implement this paperUniversal ...

Question 5

While building an auto-encoder that preserves distances, i accidentally used the euclidean norm as the loss for the difference between the x and z distances that im trying to minimize. (I hope you can ...

Question 6

I am trying to implement a custom loss function inspired by https://arxiv.org/pdf/2305.10464.pdf. That is: $ L(\mathbf{x}) = (1-y) \left\lVert \mathbf{x_{true} - \mathbf{x_{pred}}} \right\rVert^2 + y \...

Question 7

I have a set of ~ 5000 greyscale images with resolution of 64x128. I want to do an unsupervised anomaly detection. As a first try, I chose convolutional autoencoders (AE) and trained an AE model. I ...

Question 8

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*...

Question 9

I am trying to implement a paper in which the ultimate goal is to predict mutliple labels for instances (which are genes here). The feature matrix with shape of 1236*18930 is built by calculating term ...

Question 10

I am building a denoising autoencoder to repaint lanes from a binary image. The input is a binary image that has incomplete lanes, due to vehicles getting in the way. I repaint the lanes manually so ...

Question 11

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Question 12

I am trying to apply an autoencoder for feature extraction with the input like I=[x1,x2,x3,...,xn]. Representing the latent code after encoding as L, I want to improve the influence of one element of ...

Question 13

As I understanding the VAE, it's a model to get the P(x) of x(final job like image generation). When i train it, It input x from dataset to get mu and var from encoder, and to get a sample z from mu ...

Question 14

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 ...

Question 15

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 ...

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Questions tagged [autoencoder]

Possible to Improve Reconstruction Quality and Accuracy with VAE?

Misleading representation for autoencoder

Predicting pregnancy codes with transformer

Why does my reconstruced image appears darker than the original?

Can you use the Euclidean Distance as a loss function?

Custom loss function in python

Autoencoders are fitting anomalies too good

Losing Information while resizing the image in Segmentation task using U-net

Why are some columns of feature matrix after dimentionality reduction zero?

Trying to train a denoising autoencoder to restore missing information from a binary image

I can't understand why the validation and training loss almost constant and not converging?

How to improve the influence of one element of the input on the latent code in an autoencoder?

Not understanding how to eval a VAE model?

Unsupervised Machine Translation System Using Variational Autoencoder Models

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

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