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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2answers
127 views

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 ...
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1answer
564 views

Keras - Autoencoder different from Encoder + Decoder

I build a CNN 1d Autoencoder in Keras, following the advice in this SO question, where Encoder and Decoder are separated. My goal is to re-use the decoder, once the Autoencoder has been trained. The ...
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1answer
45 views

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 ...
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0answers
13 views

How to shape data when using LSTM autoencoder

I am working with simple data that has multiple features and a time stamp column. I have 24 hours of data across 70 days. The total number of samples is 1680. When applying a LSTM autoencoder, how ...
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1answer
2k views

How to set the Reconstruction error threshold for anomaly detection using autoencoders?

Hi I am doing anomaly detection using auto encoders.I have trained the model using 'Non Anomalous' values.Now when I give anomalous points as test data. What should be the Reconstruction error ...
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1answer
34 views

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)....
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1answer
634 views

Autoencoder implementation using ImageDataGenerator

I'm using the concept demonstrated in this paper. Their training data consists of "GOOD" images and "BAD" images. They train the AE using "BAD" images (X) to make it ...
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2answers
101 views

two autoencdoers learned by two similar vectors (each one with its own). Similarity of hidden layer vectors will be the same?

If I will train one autoencoder with one vector only and a second autoencoder with a second vector only, does it mean if vectors were similar, that the hidden layer vectors of both autoencoders will ...
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3answers
2k views

How to use Autoencoders for outlier detection on images

I have a bunch of images taken from a camera showing a pipe and would like to detect if the pipe is leaking or not. There are very few examples of leaking pipes in the data set. So considering this ...
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32 views

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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0answers
11 views

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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1answer
40 views

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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2answers
380 views

Cross-Validation in Anomaly Detection with Labelled Data

I am working on a project where I train anomaly detection algorithms Isolation Forest and Auto-Encoder. My data is labelled so I have the ground truth but the nature of the problem requires ...
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8 views

Deep learning model for very sparse object detection in noisy images

I am trying to build a model that takes in very noisy 200x200 greyscale images of spatially sparse objects and attempts to localise them with bounding boxes. The objects are very thin streaks (data of ...
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1answer
3k views

Autoencoders for the compression of time series

I am trying to use autoencoder (simple, convolutional, LSTM) to compress time series. Here are the models I tried. Simple autoencoder: ...
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1answer
42 views

Keras: Very high loss for Autoencoder

I am trying to implement an autoencoder for prediction of multiple labels using Keras. This is a snippet: ...
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1answer
31 views

Ngram based Langauge Models learned using an Encoder-Decoder Model

I have been going through a Ngram based Langauge Model learned using an Encoder-Decoder Model for Email smart compose. The program output only 1 prediction for given input. I want to know how to ...
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1answer
177 views

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 ...
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1answer
50 views

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 ...
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15 views

Model accuracy and validation accuracy stuck at constant value for denoising autoencoder model

I am building a denoising autoencoder (DAE) to denoise respiratory signals. I pass through the model both noisy and clean versions of the signal (in frame sizes as multiples of 1024). I've set up my ...
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1answer
114 views

Training a Variational Autoencoder (VAE) for Random Number Generation

I have a complicated 20-dimensional multi-modal distribution and consider training a VAE to learn an approximation of it using 2000 samples. But particularly, with the aim to subsequently generate ...
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0answers
11 views

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 ...
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2answers
75 views

What methods are used to determine acceptable loss levels for autoencoders?

I've been looking at autoencoders for disparate uses such as dimension reduction, blurring or sharpening images and data denoising. What methods are used to determine acceptable loss levels for ...
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1answer
19 views

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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1answer
34 views

Large scale Autoencoder for friendship recommendation

I have a user friendship graph for about 30 Million users. I am trying to use an auto encoder [30 Million, 512, 512, 1024, Dropout(0.3), 512, 512, 30 Million]. But I am not learning anything. Has ...
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0answers
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Understanding forward process in diffusion models

I was reading a blog on diffusion models where I came across this expression. I didn't understand why it is \begin{align} \sqrt[]{1-\beta \small{t}}*\large{x}\small{t-1} \end{align} and what exactly ...
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1answer
2k views

Variational AutoEncoder giving negative loss

I'm learning about variational autoencoders and I've implemented a simple example in keras, model summary below. I've copied the loss function from one of Francois Chollet's blog posts and I'm getting ...
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1answer
747 views

1D CNN Variational Autoencoder Conv1D Size

I am trying to create a 1D variational autoencoder to take in a 931x1 vector as input, but I have been having trouble with two things: Getting the output size of 931, since maxpooling and upsampling ...
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15 views

Converting lists of categorical data to numeric vectors with unlabeled data

I am preparing some data for an autoencoder. One of the variables, diag_codes, is a list of codes associated with each observation. They are of varying lengths but have at least one. My question is, ...
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2answers
587 views

Which algorithm can be used to reduce dimension of multiple time series?

In my dataset, a data point is essentially a Time series of 6 feature over a year per month so in all, it results in 6*12=72 features. I need to find class outliers so I perform dimensionality ...
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2answers
61 views

What Non-linearities are best in Denoising RNN Autoencoders and where should the go?

I’m employing a denoising RNN autoencoder for a project relating to motion capture data. This is my first time using auto encoder architectures and I was just wondering what non-linearities should be ...
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0answers
11 views

selection of loss function to avoid overfitting by autoencoder in prediction a figure with a sharp rise

I have to select the loss function to avoid overfitting by autoencoder in prediction of this figure that has a sharp raise, I would like to find how to avoid overfitting by autoencoder in prediction a ...
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1answer
634 views

How to use Variational Autoencoder's μ and σ with user-generated z?

My understanding of VAE is that unlike Autoencoders, it does not directly give you a discrete encoding (latent code vectors n-dim) instead, it gives you both mu and sigma (n- dim mean vectors and n-...
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10 views

Deep autoencoder: validation loss doesn't change

I'm trying to understand autoencoders and reproduced some code from Keras documentation: ...
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0answers
33 views

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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0answers
12 views

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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14 views

How to improve my deep LSTM model for time series?

I want to train a deep model for my time series power consumption dataset. I have created a model consist of CNN, BILSTM, Encoder-Decoder, and dense layers. here is my model: ...
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1answer
41 views

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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18 views

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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1answer
3k views

Variational Autoencoder TIme Series

Can anyone suggest a blog where Variational Autoencoder has been used for time series forecasting?
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12 views

Autoencoder in keras and accuracy

I am looking at Autoencoders in keras. They say, "Autoencoding" is a data compression algorithm where the compression and decompression functions are 1) data-specific, 2) lossy, and 3) ...
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1answer
81 views

Encoder-Decoder performance time

I have two encoder-decoder models. *First model: *Second model: When I check the performance of the models I get approximately the same performance time (First model ~ 42 sec, Second model ~ 40 ...
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15 views

Generate full face from half face image in Tensorflow

Has anyone found any examples of how an ML model can generate an image of a complete face using only an image of half a face? I figured this would be a case I'd be able to find easily on Google but I ...
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1answer
77 views

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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1answer
179 views

Preserve colour in convolutional autoencoder

at the moment i work with convolutional autoencoder and now I'am looking for paper or methods that adresses a colour preversation. Most of the AE paper use grayscale images and loss functions such as ...
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2answers
5k views

What is the difference between an autoencoder and an encoder-decoder?

I want to know if there is a difference between an autoencoder and an encoder-decoder.
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2answers
196 views

MNIST data shape

In going through the different tutorials on CNN, autoencoders, and so on I trained myself on the MNIST problem. The different images are stored in a 3D array which shape is (60000,28,28). In some ...
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0answers
414 views

Fraud detection using auto-encoders and Keras

I am following this example to learn a bit about the use of auto-encoders in fraud detection. Now that I reached the end of the article, two questions rose in mind: Can we train the network in an ...

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