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Deep Learning Project Report: June 2016 PDF

4 Pages·2019·English
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by ['Bandarapu shirisha']| 2019| 4 pages| English

About Deep Learning Project Report: June 2016

This document describes a deep learning project report that used two datasets (birds and bottles) to train and test a model for recognizing objects in tiny images. The model pipeline included feature extraction using HOG and wavelets, feature quantization and fusion, training an autoencoder, and using logistic regression for classification. Testing accuracy was highest at 21.4% when combining HOG and wavelet features, compared to 16.2% for HOG alone and 17.55% for wavelets alone. The report concludes that feature fusion performed better than individual features for object recognition with tiny images.

Detailed Information

Author:['Bandarapu shirisha']
Publication Year:2019
Pages:4
Language:English
Format:PDF
Price:FREE
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