MSc graduate

AG 1, AG 2, AG 3, INET, AG 4, AG 5, SWS, RG1, MMCI   Info

Public Audience

English

60 Minutes

Saarbrücken

Interpreting the iterative algorithm ISTA as an unfolded Deep Neural Network (DNN), a novel architecture was proposed, in which, the activation functions are analogous to the proximal operators. Unlike the standard DNNs, in this architecture, the weights and biases were kept fixed by using prior knowledge, while the activation functions were learned from the data. This lead to a rich variety of proximal operators that are suitable, in particular, for sparse coding. Consequently, the proposed network outperformed state-of-the-art sparse coding algorithms by a margin of 4 to 6 dB. This architecture is further extended for Dictionary learning by borrowing ideas from Autoencoders, where the encoding part is performed by the proposed model. This extended model learns dictionaries with which data can be represented sparsely in an unsupervised manner.

Stephanie Jörg

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