This chapter will be cover image classification using Deep learning, and why CNNs disrupted the way we do computer vision now. The references for this chapter are:
- Learning Multiple Layers of Features from Tiny Images, Alex Krizhevsky, 2009
- An excellent flashback on image representation techniques can be found in the Computer Vision: Algorithms and Applications, Richard Szeliski, 2010
- http://www.vision.caltech.edu/Image_Datasets/Caltech101/
- Griffin, Gregory and Holub, Alex and Perona, Pietro (2007) Caltech–256 Object Category Dataset
- Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J. and Zisserman, A International Journal of Computer Vision, 88(2), 303-338, 2010
- ImageNet Large Scale Visual Recognition Challenge, IJCV, 2015
- https://wordnet.princeton.edu/
- What Does Classifying More Than 10,000 Image Categories Tell Us? Jia Deng, Alexander C. Berg, Kai Li, and Li Fei-Fei
- Olga Russakovsky, Jia Deng et al. (2015) ImageNet Large Scale Visual Recognition Challenge, https://arxiv.org/pdf/1409.0575.pdf
- Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton, ImageNet Classification with Deep Convolutional Neural Networks, 2012
- https://arxiv.org/pdf/1311.2901.pdf
- Going deeper with convolutions by Christian Szegedy Google Inc. et al
- Deep Residual Learning for Image Recognition, Kaiming He et al.
- https://arxiv.org/pdf/1709.01507.pdf
- The batch norm paper is a really well written paper that is easy to understand and explains the concept in much more detail, https://arxiv.org/pdf/1502.03167.pdf