Del i serien SpringerBriefs in Computer Science

Composing Fisher Kernels from Deep Neural Models

A Practitioner's Approach

Häftad, Engelska, 2018

699 kr

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

Produktinformation

Utgivningsdatum2018-09-05
Mått155 x 235 x 5 mm
Vikt147 g
FormatHäftad
SpråkEngelska
SerieSpringerBriefs in Computer Science
Antal sidor59
FörlagSpringer International Publishing AG
ISBN9783319985237

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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.