If you’ve heard about the transposed convolution and got confused about what it actually means, this article is written for you.
The content of this article is as follows:
- The Need for Up-sampling
- Why Transposed Convolution?
- Convolution Operation
- Going Backward
- Convolution Matrix
- Transposed Convolution Matrix
- Summary
The notebook is available on my GitHub.
The Need for Up-sampling
When we use neural networks to generate images, it usually involves up-sampling from low resolution to high resolution.
There are various methods to conduct up-sampling operations:
- Nearest neighbor interpolation
- Bi-linear interpolation
- Bi-cubic interpolation
All these methods involve some interpolation method we must choose when deciding on a network architecture. It is like manual feature engineering, and there is nothing that the network can learn about.
