id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_omriqrr3svfvneiuas2o437ym4	Atul Laxman Katole	Hierarchical Deep Learning Architecture For 10K Objects Classification	2015.0	17	.pdf	application/pdf	6160	541	60	principle that decomposes the large scale recognition architecture into root & leaf level model propose a blend of leaf level models trained with either supervised or unsupervised learning proposed method is the first attempt to classify 10K objects utilizing a two level hierarchical deep Also a blend of supervised & unsupervised learning based leaf level models We have not come across any work that uses 2-level hierarchical deep learning architecture to Supervised learning based deep visual recognition CNN architectures are composed of multiple CDBN based leaf level models can be trained with unsupervised learning approach in case of recognition models in our two-level hierarchical architecture is trained utilizing supervised The root level & the leaf level CNN models in our architecture are trained with supervised We train the first two layers in the leaf architecture with unsupervised learning. Proposed 2 Level Hierarchical Deep Learning Architecture constructed entirely utilizing CDBNs 	./cache/work_omriqrr3svfvneiuas2o437ym4.pdf	./txt/work_omriqrr3svfvneiuas2o437ym4.txt