Questions tagged [autoencoder]
An autoencoder, autoassociator or Diabolo network is an artificial neural network used for learning efficient codings. As such, it is part of the dimensionality reduction algorithms.
autoencoder
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Detecting unknown defects in toy car using deep learning methods [closed]
We need to determine from images of toy cars whether the cars are defective or not. We don't have images of defective cars, and we can't know in advance what kinds of defects these cars might have.
We ...
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How to apply augmentations to batch on each step of epoch in Keras?
I'm trying to solve an anomaly detection task using an autoencoder model, but I'm getting poor results after training. My teacher suggested I use data augmentation to batch on each step during epoch. ...
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Recreating Text Embeddings From An Example Dataset
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 struggling. While the model runs ...
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Vanishing Gradient in Autoecnoder
I am trying to train a convolutional autoencoder to classify signals based on their shapes. My dataset consist of sinusoidal signals which have two different frequencies but their phases are ...
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Multi-task learning- Loss function
I am training a convolutional autoencoder with two velocity fields as inputs and outputs. These fields represent wind velocities in both the x and y directions within a square domain. My loss function ...
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How to solve exploding gradient problem in VAE training?
I was trying to implement VAE on the CelebA dataset inspired by the Tensorflow implementation of MNIST. I have tried varying batch size but there seems to be no effect from that. The image formed is ...
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Contrastive Autoencoder loss in pytorch
I am working on an autoencoder project and would like to understand how to implement a contrastive loss for it.
As far as I understand, a contrastive loss uses pairs of latent space representations ...
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Workaround for removal of add_loss()
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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Why does AutoModel assign the XLMRobertaModel class to the model when I load E5?
Why does AutoModel assign the XLMRobertaModel class to the model when I load E5? Is E5 a separately trained XLMRobertaModel encoder? Will I lose knowledge of e5 when I initialize it like this?
from ...
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Convolutional Variational Autoencoder
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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How to Implement Autoencoders for Prediction and Inverse Design Problems?
I'm exploring the use of autoencoders to address two types of problems: prediction and inverse design. Specifically, I want to:
a.Set input variables and use the trained decoder of an autoencoder to ...
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PyG graph autoencoder loss is frozen, possible Data object assembly issue
I'm trying to use Graph Autoencoder on a custom PyG Data object, but when I attempt to train it, the loss, AUC and AP do not change. The exact same autoencoder works when using PyTorch Geometric's ...
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Output of autoencoder is independent of input to autoencoder
I am writing a convolutional autoencoder for a 3D input with 4 channels.
The encoder part is composed of a number of blocks, where each block contains a 3D convolution layer, a ReLU activation layer, ...
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Input mismatch in dense layer
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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LSTM autoencoder for time series forecasting
I am trying to develop an LSTM autoencoder for time series forecasting using three different datasets (A: heatup, B: coldtrap, C: filling). I will train the model on data from dataset A and also on ...
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