什么是卷积
目录
- Feature maps
- Why not Linear
- 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
更多精彩
