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Sampling From Autoencoders' Latent Space via Quantization And Probability Mass Function Concepts
Furthermore, when it comes to generating images of faces and ocular images, our approach showcases substantial enhancements with FID improvements of $1. 69$ and $0. 87$ respectively, as compared to GMM sampling, as evidenced on the CelebA and MOBIUS datasets.
Simplex Autoencoders
In this work, we propose a new approach that models the latent space of an Autoencoder as a simplex, allowing for a novel heuristic for determining the number of components in the mixture model.
A Pseudo-labelling Auto-Encoder for unsupervised image classification
In this paper, we introduce a unique variant of the denoising Auto-Encoder and combine it with the perceptual loss to classify images in an unsupervised manner.
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