目录

Feature maps Why not Linear 335k or 1.3MB em... Receptive Field Fully connnected Partial connected Locally connected Rethink Linear layer Fully VS Lovally Weight sharing Why call Convolution？ 2D Convolution Convolution in Computer Vision CNN on feature maps

Feature maps

单通道

rgb三通道

rgb三通道合成

数字2的卷积成像图

Why not Linear

4 Layers: [784, 256, 256, 256, 10]

335k or 1.3MB

em...

486 PC + AT&T DSP32C

256KB 66Mhz

Batch X

Gradient Cache

etc.

Receptive Field

Fully connnected

Partial connected

Locally connected

Rethink Linear layer

Fully VS Lovally

Weight sharing

三阶张量的卷积

6 Layers

~60k parameters

4 layers, 335k

Why call Convolution？

2D Convolution

\[y(t) = x(t)*h(t) = \int_{-\infty}^{\infty}x(\tau)h(t-\tau)d\tau \]

Convolution in Computer Vision

模糊化

边缘检测

CNN on feature maps

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