Composing Fisher Kernels from Deep Neural Models: A Practitioner's Approach

ISBN-13: 9783319985237 / Angielski / Miękka / 2018 / 59 str.

Tayyaba Azim; Sarah Ahmed

Opis

This book shows machine learning enthusiasts and practitioners how to get the best of both worlds by deriving Fisher kernels from deep learning models.

O autorze

Dr. Tayyaba Azim is an Assistant Professor at the Center for Information Technology, Institute of Management Sciences, Peshawar, Pakistan. 

Sarah Ahmed is a current research student enrolled in Masters of Computer Science program at Institute of Management Sciences Peshawar, Pakistan. 

She has received her  Bachelor’s Degree in Computer Science from Edwardes College, Peshawar,Pakistan. Her areas of interest include: Machine Learning, Computer Vision and Data-Science. Currently, her research work is centered around the feature compression and selection approaches for Fisher vectors derived from deep neural models. Her research paper: "Compression techniques for Deep Fisher Vectors" was awarded  the best paper in the area of applications at ICPRAM conference 2017.