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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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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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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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 ...
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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 ...
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Mix of time-dependent and constant features for a transformer

I'm using the transformer architecture to predict future time-points from previous time-points. Each item of the input sequence is a vector of ...
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KL divergence loss first decreases and then increases in VAE training

I am training a VAE on CelebA HQ (resized to 256x256). The training is going well, the reconstruction loss is decreasing and reconstructions are also meaningful. But, the problem is with KL divergence ...
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An autoencoder setup for anomaly detection

I am doing anomaly detection using machine learning. i have tried different models like isolation forest, SVM and KNN. The maximum accuracy that I can get from each of them is $80\%$ accordind to my ...
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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 ...
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Training data for anomaly detection using LSTM Autoencoder

I am building an time-series anomaly detection engine using LSTM autoencoder. I read this article where the author suggests to train the model on clean data only in response to a comment. However, in ...
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Incremental learning on Autoencoder for anomaly detection

I want to incrementally train my pre-trained autoencoder model on data being received every minute. Based on this thread, successive calls to model.fit will incrementally train the model. However, the ...
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Does Sliced Wasserstein Distance work in higher than 2 dimensions?

I had thought that it only worked for 2D distributions. I am trying to implement a sliced Wasserstein autoencoder and I was wondering if my latent space can be larger than 2D.
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Latent space vs Embedding space | Are they same?

I am going through variational autoencoders and it is mentioned that: continuity (two close points in the latent space should not give two completely different contents once decoded) and completeness ...
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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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Generate new distribution from auto-encoder /variational autoencoder

I know that autoencoders can be used to generate new data. From what I could understand.. The autoencoder uses the original distribution X to learn a random gaussian distribution described by mean and ...
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Variational Autoencoders VS Transformers

I'm relatively new to the field, but I'd like to know how do variational autoencoders fare compared to transformers?
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CNN auto-encoder performs much worse than Fully connected auto-encoder

I am trying to develop a one-class classifier, that will learn regular examples, and, hopefully, will have hard times reconstructing anomaly observations. I have 1D signals which I tried to ...
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Can we use autoencoders to change an existing image instead of create one from scratch?

I'm trying to think if we can use autoencoders to edit an existing image instead of saying, creating a new one from scratch. To give an example, say I train my data on the MNIST dataset. If I now give ...
Vishal Balaji's user avatar
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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 ...
BlackCode's user avatar
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What does it means (concretly) that a VAE encode inputs as distribution?

From this post we can read that VAEs encode inputs as distributions instead of simple points ? What does it mean concretely ? If the encoder consists of the weights between the input image and the ...
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quereies related to autoencoder

i want to design an deep auto encoder after following keras tutorial. Input is a simple 2-dimensional image consists of 512 rows and 50 columns matrix My trial code is ...
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how an autoencoder denoise an image

i am using denoising autoencoder to denoise the image in the unsupervised way.But still after implementation of the denoisng autoencoder i am unable to understand how an autoencoder network know which ...
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how to set threshold value by looking at loss distribution in anomaly detection task

I am following this tutorial https://towardsdatascience.com/lstm-autoencoder-for-anomaly-detection-e1f4f2ee7ccf to use LSTM autoencoder to detect anomalies in my unsupervised dataset. they plotted ...
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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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ValueError: Input 0 of layer conv2d is incompatible with the layer: expected axis -1 of input shape to have value 1 but received input with shape

I'm trying to create an auto-encoder based model for segmentation, which looks something like this: https://i.stack.imgur.com/4F3Z0.png I haven't added a single step, nor missed one as far as I ...
Maifee Ul Asad's user avatar
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input of Auto-Encoder as a feature extraction for training is similar to data that we use later for a classification model?

I have a data set of images, for example, 200 images, I want to use Autoencoder as a feature compressor. I use for example 150 for train the autoencoder and 50 for evaluation. after train and evaluate ...
Shiva Parsa Rad's user avatar
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Multioutput prediction using LSTM encoder decoder with Attention

(I am working on Jupter notebook with python version 3.6.12, running Tensorflow 2.4.0 version.) I have a dataset that consists of 5 input features and 3 output features (that requires to be predicted)....
Sukhmani Kaur Thethi's user avatar
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Is the explained variance a good metric for autoencoders?

I want to evaluate how an autoencoder will perform on my data. Now, I can do this with the mean squared error of the decoded data compared to the original data, and this is fine when comparing this ...
João Areias's user avatar
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Correct approach to scale (min-max scaler) both input and output signal data for unsupervised learning?

I am working on a denoising autoencoder problem with noisy and clean signals. Before I pass the signals to my model I want to apply min-max normalization and am unsure of the correct way to apply this....
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Deep autoencoder: validation loss doesn't change

I'm trying to understand autoencoders and reproduced some code from Keras documentation: ...
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Word embedding autoencoder

I'm trying to train a word embedding autoencoder, but it either doesn't train, or trains but doesn't make predictions. I know I'm doing something wrong, so any help is greatly appreciated. Here is my ...
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Trouble with anomaly/novelty detection (on microscale) - need easy practical guide with Keras

I am relatively new to the field of machine learning. However, I already have solved simple image classification tasks with Keras (for example building CNNs and classifying MNIST...). The rough deep ...
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Generating unique points with an auto-encoder

I have been working on some research using a type of auto-encoder to generate new points with specific desirable properties. I trained my network and successfully generated some points, but when I ...
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Custom keras callbacks and changing weight (beta) of regularization term in variational autoencoder loss function

The variational autoencoder loss function is this: Loss = Loss_reconstruction + Beta * Loss_kld. I am trying to efficiently implement Kullback-Liebler Divergence Cyclic Annealing--that is changing the ...
Jared Frazier's user avatar
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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/...
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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 ...
Shameel Faraz's user avatar
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If the input to the autoencoder is normalized, do we need to use sigmoid on the last layer?

According to: https://stackoverflow.com/questions/65307833/why-is-the-decoder-in-an-autoencoder-uses-a-sigmoid-on-the-last-layer The last layer activation function contains sigmoid in order to the ...
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Explainability and Autoencoders

suppose I have an autoencoder as a two-stack LSTM that takes in sequences of $n$ features of some length $m$. Let's say that the dimension of my encoding vector is $k$, so the architecture is of the ...
Mariah's user avatar
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Why autoencoders use binary_crossentropy loss and not mean squared error?

Keras autoencoders examples: (https://blog.keras.io/building-autoencoders-in-keras.html) use binary_crossentropy (BCE) as loss function. Why they use ...
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How to interpreter Binary Cross Entropy loss function?

I saw some examples of Autoencoders (on images) which use sigmoid as output layer and BinaryCrossentropy as loss function. The ...
Boom's user avatar
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Convolutional autoencoder - why keras example is asymmetry model?

I'm looking on keras convolutional autoencoder example, and confused with the model structure: ...
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Autoencoder: Size of out_backprop doesn't match computed

This question was asked before and non of the answered worked for, I have the code ...
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1 answer
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Anomaly detection using LSTM AutoEncoder

Having a sequence of 10 days of sensors events, and a true / false label, specifying if the sensor triggered an alert within the 10 days duration: sensor_id timestamp feature_1 feature_2 ...
Shlomi Schwartz's user avatar
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How to derive Evidence Lower Bound in the paper "Zero-Shot Text-to-Image Generation"?

Can someone share the derivation of Evidence Lower Bound in this paper ? Zero-Shot Text-to-Image Generation The overall procedure can be viewed as maximizing the evidence lower bound (ELB) (Kingma &...
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Autoencoder: good loss values while fitting, but the actual performance is bad

I'm implementing a 1D convolutional autoencoder to reduce the dimensionality of an array. Here is my architecture: ...
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GAN model with different optimization functions

Building GAN model contains the following steps: Build generator model, and choose ...
user3668129's user avatar
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Autoencoder give wrong results

I have studied Autoencoders and tried to implement a simple one. I have built a model with one hidden layer. I ran it with the MNIST digits dataset and plotted the digits before the Autoencoder and ...
Boom's user avatar
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Latent variable graph in Variational Autoencoder

I followed this Keras documentation guide about Auto Encoders. At the end of the documentation there is the graph of the latent variable z: But I can not understand and how to interpret the plot, ...
Turned Capacitor's user avatar
1 vote
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Autoencoder train and test accuracy shooting to 99% on few epochs

I am trying to train an autoencoder for dimensionality reduction and hopefully for anomaly detection. My data specifications are as follows. Unlabeled 1 million data points 9 features I am trying to ...
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