Questions tagged [autoencoder]
Autoencoders are a type of neural network that learns a useful encoding for data in an unsupervised manner.
326 questions
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Tips for increasing classification performance
I'm training a Multi View CNN on a ModelNet10 dataset (5000 3d meshes of 10 classes) to later use it as an encoder part for AutoEncoder model for 3d reconstruction. Each element of dataset is 40 ...
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Predicting pregnancy codes with transformer
Im trying to predict pregnancy codes with a basic transformer model architecture. These pregnancy codes are like following prg001, prg002 to prg030. Prg001 would be antenatal screening and prg030 ...
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Why does my reconstruced image appears darker than the original?
I am trying build an autoencoder which would encode the image into latent space and then decode it back to the original image without any changes. I am mainly trying to implement this paperUniversal ...
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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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