01481nas a2200205   4500000000100000000000100001008004100002260004200043100002000085700002300105700002400128700002000152700002200172700001900194245010400213856003700317490001000354520088900364022002201253       2022                            d  c05/2022bSpringer, ChamaLecce, Italy1 aKamil Książek1 aPrzemysław Głomb1 aMichał Romaszewski1 aMichał Cholewa1 aBartosz Grabowski1 aKrisztian Buza00aImproving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation  uhttps://arxiv.org/abs/2109.137480 v132313 a<p>Neural networks, in particular autoencoders, are one of the most promising solutions for unmixing hyperspectral data, i.e. reconstructing the spectra of observed substances (endmembers) and their relative mixing fractions (abundances), which is needed for effective hyperspectral analysis and classification. However, as we show in this paper, the training of autoencoders for unmixing is highly dependent on weights initialisation; some sets of weights lead to degenerate or low-performance solutions, introducing negative bias in the expected performance. In this work, we experimentally investigate autoencoders stability as well as network reinitialisation methods based on coefficients of neurons’ dead activations. We demonstrate that the proposed techniques have a positive effect on autoencoder training in terms of reconstruction, abundances and endmembers errors.</p>
  a978-3-031-06427-2