Inception (deep learning architecture)
Inception v3[1] is a convolutional neural network for assisting in image analysis and object detection, and got its start as a module for Googlenet. It is the third edition of Google's Inception Convolutional Neural Network, originally introduced during the ImageNet Recognition Challenge. Just as ImageNet can be thought of as a database of classified visual objects, Inception helps classification of objects[2] in the world of computer vision. One such use is in life sciences, where it aids in the research of leukemia.[3] The original name (Inception) was codenamed this way after a popular "'we need to go deeper' internet meme" went viral, quoting a phrase from Inception film of Christopher Nolan.[4]
References
- ^ Tang (May 2018). Intelligent Mobile Projects with TensorFlow. Packt Publishing. pp. Chapter 2. ISBN 9781788834544.
- ^ Karim and Zaccone (March 2018). Deep Learning with TensorFlow. Packt Publishing. pp. Chapter 4. ISBN 9781788831109.
- ^ Milton-Barker, Adam. "Inception V3 Deep Convolutional Architecture For Classifying Acute Myeloid/Lymphoblastic Leukemia". intel.com. Intel. Retrieved 2 February 2019.
- ^ Szegedy, Christian (2015). "Going deeper with convolutions" (PDF). CVPR2015.
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