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2023
Report
Open Access
Are we using autoencoders in a wrong way?
Martino G., Moroni D., Martinelli M.
Autoencoders are certainly among the most studied and used Deep Learning models: the idea behind them is to train a model in order to reconstruct the same input data. The peculiarity of these models is to compress the information through a bottleneck, creating what is called Latent Space. Autoencoders are generally used for dimensionality reduction, anomaly detection and feature extraction. These models have been extensively studied and updated, given their high simplicity and power. Examples are (i) the Denoising Autoencoder, where the model is trained to reconstruct an image from a noisy one; (ii) Sparse Autoencoder, where the bottleneck is created by a regularization term in the loss function; (iii) Variational Autoencoder, where the latent space is used to generate new consistent data. In this article, we revisited the standard training for the undercomplete Autoencoder modifying the shape of the latent space without using any explicit regularization term in the loss function. We forced the model to reconstruct not the same observation in input, but another one sampled from the same class distribution. We also explored the behaviour of the latent space in the case of reconstruction of a random sample from the whole dataset.Source: ISTI Working papers, 2023
DOI: 10.48550/arxiv.2309.01532
Metrics:
Martino G., Moroni D., Martinelli M.
Autoencoders are certainly among the most studied and used Deep Learning models: the idea behind them is to train a model in order to reconstruct the same input data. The peculiarity of these models is to compress the information through a bottleneck, creating what is called Latent Space. Autoencoders are generally used for dimensionality reduction, anomaly detection and feature extraction. These models have been extensively studied and updated, given their high simplicity and power. Examples are (i) the Denoising Autoencoder, where the model is trained to reconstruct an image from a noisy one; (ii) Sparse Autoencoder, where the bottleneck is created by a regularization term in the loss function; (iii) Variational Autoencoder, where the latent space is used to generate new consistent data. In this article, we revisited the standard training for the undercomplete Autoencoder modifying the shape of the latent space without using any explicit regularization term in the loss function. We forced the model to reconstruct not the same observation in input, but another one sampled from the same class distribution. We also explored the behaviour of the latent space in the case of reconstruction of a random sample from the whole dataset.Source: ISTI Working papers, 2023
DOI: 10.48550/arxiv.2309.01532
Metrics:
See at:
ISTI Repository 
| CNR ExploRA
2023
Software
Unknown
VAE for dummies
Martino G.
Semplice implementazione di un Variational Autoencoder (VAE) in Pytorch Lightning con un applicativo interattivo ausiliario per esplorare in tempo reale lo spazio latente del VAE.
Martino G.
Semplice implementazione di un Variational Autoencoder (VAE) in Pytorch Lightning con un applicativo interattivo ausiliario per esplorare in tempo reale lo spazio latente del VAE.
See at: github.com | CNR ExploRA
2023
Software
Unknown
Image segmentation tool
Martino G.
Software sviluppato in Python con GUI che permette la creazione di dataset per la segmentazione di immagini e l'esportazione in formato COCO.
Martino G.
Software sviluppato in Python con GUI che permette la creazione di dataset per la segmentazione di immagini e l'esportazione in formato COCO.
See at: github.com | CNR ExploRA