Enhanced Deep Learning with Improved Feature Subspace Separation

Parlaktuna, Mustafa
Sekmen, Ali
Koku, Ahmet Buğra
Abdul Malek, Ayad
This research proposes a new deep convolutional network architecture that improves the feature subspace separation. In training, the system considers M classes of input sets {C-i}(i=1)(M) and M deep convolutional networks {DNi}(i=1)(M) whose filter and other parameters are randomly initialized. For each input class C-i, Convolutional Neural Network generates a set of features F-i. Then, a local subspace S-i is matched for each set F-i. This is followed with a full training of the deep convolutional network DNi based on a decision criteria developed with computation of rejections of all features in {F-i}(i=1)(M) to S-i. Five different deep convolutional network topologies are used to show that the proposed technique works better for small network topologies and has comparable performance to more complex networks.
Citation Formats
M. Parlaktuna, A. Sekmen, A. B. Koku, and A. Abdul Malek, “Enhanced Deep Learning with Improved Feature Subspace Separation,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55116.