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What and Who
Title:DNNs for Sparse Coding and Dictionary Learning
Speaker:Debabrata Mahapatra
coming from:Indian Institute of Science Bangalore
Speakers Bio:MSc graduate
Event Type:PhD Application Talk
Visibility:D1, D2, D3, INET, D4, D5, SWS, RG1, MMCI
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Level:Public Audience
Date, Time and Location
Date:Monday, 8 October 2018
Duration:60 Minutes
Building:E1 5
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.
Name(s):Stephanie Jörg
Phone:0681 9325 1800
EMail:--email address not disclosed on the web
Video Broadcast
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Attachments, File(s):
Stephanie Jörg/MPI-INF, 10/05/2018 12:12 PM
Last modified:
Stephanie Jörg/MPI-INF, 10/08/2018 09:41 AM
  • Stephanie Jörg, 10/08/2018 09:42 AM
  • Stephanie Jörg, 10/05/2018 12:18 PM -- Created document.