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GENERATIVE SCATTERNET HYBRID DEEP LEARNING (G-SHDL) NETWORK…
www-sigproc.eng.cam.ac.uk/foswiki/pub/Main/NGK/Singh-1802.03374-ICASSP_2018.pdf28 Dec 2018: The handcrafted features extracted at the three layersare concatenated and given as input to the 4 stacked convolutional RBM layers (L3, L4, L5, L6) with 200, 150, 100 and 50 filters ... Arch. 50 100 200 300 400 500 572G-SHDL 40.3 59.9 66.4 72.6 75.7 -
To Appear in the 27th IEEE International Workshop on ...
www-sigproc.eng.cam.ac.uk/foswiki/pub/Main/NGK/Singh-1708.09212-MLSP_2017.pdf28 Dec 2018: Fig. 3(b)). L4 PCA layer is trained on L3 layer outputs (ŷL3,rs) cor-respondingly to learn 200 (KL4) filters, of size 5 (sL4). -
DEEP LEARNING IN THE WAVELET DOMAIN Fergal Cotter and ...
www-sigproc.eng.cam.ac.uk/foswiki/pub/Main/NGK/Cotter-1811.06115-arXiv.pdf28 Dec 2018: Wetrain both networks for 200 epochs with Adam [8] optimizerwith a constant learning rate of 103 and a weight decayof 105.
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