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Recently I have been practicing with AutoEncoders and trying to use them for anomaly detection. I have been told that using a threshold on the loss to classify anomalies (calibrated with roc-curve) is ...
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I'm working on a multi-label classification problem using an autoencoder-based neural network built in PyTorch. The overall idea of my approach is as follows: I load my dataset from a CSV file, ...
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How can I compute the time complexity of the training of an Autoencoder that take in input an array of dimension 1xN and have only one hidden layer with with M neurons. So the matrices of weights are ...
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I was asked to create an Autoencoder that reconstructs the binary CSV file (decode). I implemented one based on the MNIST example from geeksforgeeks. But I am very uncertain about the correctness, ...
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I'm working through a Keras/Tensorflow course that uses Keras 2 to build a variational autoencoder and I'm trying to get it working in Keras 3. I've managed to overcome a lot of issues but I'm stuck ...
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I'm creating a Convolutional Variational Autoencoder with Tensorflow in Python code, with some images I created myself (64x64 pixels). My problem is, that if I use anything else than Binary ...
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I am using this autoencoder model to detect anomaly. class AnomalyDetector(Model): def __init__(self): super(AnomalyDetector, self).__init__() self.encoder = tf.keras.Sequential([ ...
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i am trying to build my first Autoencoder for anomaly detection and i dont really know, how the Input Data has to be shaped in order to train the Model. I`ll give you Information about the Data and i ...
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I use LSTM-Autoencoder, took the model by this guy https://colab.research.google.com/drive/1_J2MrBSvsJfOcVmYAN2-WSp36BtsFZCa#scrollTo=vgUChGd_A-Bv, for anomaly detection in time series and got problem ...
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I'm relatively new and am trying to build an autoencoder for anomaly detection on an unlabelled dataset that only contains categorical columns. The error I get is: Incompatible shapes: [64,1,5346] vs. ...
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I am trying to create a Contractive Autoencoder, and I read in a couple of papers that the main idea is to use the norm of the Jacobian of the encoder's output with respect to its inputs. In other ...
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I'm training an autoencoder to detect anomalies among pictures based on the decoder error value. I tried out different ways of image preprocessing, NN architectures, losses, activation functions, ...
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Approach: I wrote a custom loss function in PyTorch that compares the angular difference between the original (input) and reconstructed images based on their first principal component axes. This ...
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I don't understand why the mismatch of the input size and the pytorch linear layer still could be processed during the forward pass I tried my AE model with pytorch, the following is the model. I don'...
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I am working in the field of audio classification. Recently I have been trying to use Supervised VAE Classifier. Here is the architecture I am using: class VAE(nn.Module): def __init__(self, ...
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