However, there are several problems with denoising autoencoders. One problem is that in a high dimensional continuous space, there are uncountable ways to corrupt a piece of data. So there is no guarantee that we can shape the energy function by simply pushing up on lots of different locations. Another problem with the model is that it performs poorly when dealing with images due to the lack of latent variables. Since there are many ways to reconstruct the images, the system produces various predictions and doesn’t learn particularly good features. Besides, corrupted points in the middle of the manifold could be reconstructed to both sides. This will create flat spots in the energy function and affect the overall performance.
Cependant, l’auto-encodeur débruiteur pose plusieurs problèmes. L’un des problèmes est que dans un espace continu de grande dimension, il existe d’innombrables façons de corrompre une donnée. Il n’y a donc aucune garantie que nous puissions modeler la fonction d’énergie en poussant simplement sur un grand nombre d’endroits différents.