Cheat sheet for exam

이 아이들을 A4 1장에 어떻게 잘 우겨 넣을 것인가.. 돋보기 챙겨가야 하는 거 아냐..? -_- 

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* Linear classifier: One template per class
* Neural net: first layer is bank of templates; Second layer recombines templates
- Can use different templates to cover multiple modes of a class!

- “Distributed representation”: Most templates not interpretable!

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First-layer conv filters: local image templates (Often learns oriented edges, opposing colors)

For convolution with kernel size K, each element in the output depends on a K x K receptive field in the input

Each successive convolution adds K – 1 to the receptive field size. With L layers the receptive field size is 1 + L * (K – 1)

 

Input volume: 3 x 32 x 32

10 5x5 filters with stride 1, pad 2

 

Output volume size: 10 x 32 x 32
Number of learnable parameters: 760
Number of multiply-add operations: 768,000
10*32*32 = 10,240 outputs; each output is the inner product 
of two 3x5x5 tensors (75 elems); total = 75*10240 = 768K

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Most of the memory usage is in the early convolution layers

Nearly all parameters are in the fully-connected layers

Most floating-point ops occur in the convolution layers

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