Autoencoder and Partially Impossible Reconstruction Losses.

ABSTRACT: The generally unsupervised nature of autoencoder models implies that the main training metric is formulated as the error between input images and their corresponding reconstructions. Different reconstruction loss variations and latent space regularizations have been shown to improve model performances depending on the tasks to solve and to induce new desirable properties such as disentanglement. Nevertheless, measuring the success in, or enforcing properties by, the input pixel space is a challenging endeavour. In this work, we want to make use of the available data more efficiently and provide design choices to be considered in the recording or generation of future datasets to implicitly induce desirable properties during training. To this end, we propose a new sampling technique which ma

SUBMITTER: Dias Da Cruz S 

PROVIDER: S-EPMC9268944 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature