Questions tagged [autoencoder]
Autoencoders are a type of neural network that learns a useful encoding for data in an unsupervised manner.
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training convergence stagnation in image reconstruction using Vgg19
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I am trying to implement this paper Universal Style Transfer via Feature Transforms. In the paper they trained 5 decoders for ...
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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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Recreating Text Embeddings From An Example Dataset
I am in a situation where I have a list of sentences, and a list of their ideal embeddings on a 25-dimensional vector. I am trying to use a neural network to generate new encodings, but I am ...
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Choosing Between Autoencoder with OC-SVM and Reconstruction Error for Anomaly Detection: Training Strategies and Considerations
I plan to use an autoencoder and One-class SVM (OC SVM) for anomaly detection.
So there are 2 strategies:
train the autoencoder, and use the encoder output (as reduced dimension) to train an OC SVM
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Can you use the Euclidean Distance as a loss function?
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 ...
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About autoencoder's latent state regularity
Suppose we are dealing with the problem of dimensionality reduction of an input $\mathbf{x}\in\mathbb{R}^N$, by employing an autoencoder, as a composition of the encoder and decoder map $\mathbf{x} \...
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Autoencoders failing to recreate MNIST numbers
I have been having trouble trying to get a working (non-variational) autoencoder to reproduce images from the MNIST dataset. The two biggest issues is an averaging of the samples to yield a single ...
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Custom loss function in python
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 \...
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Autoencoders are fitting anomalies too good
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 ...
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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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Why are some columns of feature matrix after dimentionality reduction zero?
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 ...
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Trying to train a denoising autoencoder to restore missing information from a binary image
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 ...
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PyG Autoencoder reconstruction loss criterion node vs link prediction
In link_pred.py they use this criterion in computing the loss (which is like what we used in SRP).
criterion = torch.nn.BCEWithLogitsLoss()
https://github.com/pyg-...
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Custom loss function for collinearity of 3 embeddings
I am trying to implement a loss function that takes as input 3 embeddings and output a value that is proportional to the collinearity of the embeddings.
This is to shape the latent space of a ...
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How to improve the influence of one element of the input on the latent code in an autoencoder?
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 ...
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Can unsupervised pretraining (autoencoders) be used for u-nets?
TLDR: Will a u-net pretrained as an autoencoder be able to learn a latent representation of the data if the encoder weights are frozen (can't game the system and pass forward the unmodified image)? ...
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Not understanding how to eval a VAE model?
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 ...
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The accuracy of my model (AutoEcoder) Stops always at 50%, I want 95%
Hello StackOverflow community,
I'm working with a dataset comprising binary vectors. For each instance in my dataset, there is an input vector X and an output ...
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Can I use lstm/autoencoder to cluster multivariate time series?
I have a multivariate time series of driving scenarios which has X,Y positions, speed, orientation etc. of the vehicles. Each scenario A, B, C, D etc. are of different lengths with different delta ts ...
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The using of golden dataset in Augmented SBERT Training
I use the training strategy of Augmented SBERT (Domain-Transfer). In the code example they use the golden-dataset (STSb) for the training evaluator. Here two code snippets of the example of sentence-...
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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 ...
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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 ...
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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 ...
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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
...
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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 ...
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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 ...
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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
\...
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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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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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. ...
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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,...
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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 ...
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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 ...
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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 ...
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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 ...
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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:
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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 ...
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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 ...
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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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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 ...
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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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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 ...
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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 ...
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