Covers: implementation of Speeding up your learning
0Questions this item addresses:
- What are the ways that I can parallelize and speed up my GANs' training?
How to use this item?
Depending on how your network is structured, you can do a drop-in-replace with DeepSpeed in a couple of hours. This will likely speed up your learning a LOT (>3X in my case) vs. using vanilla pytorch
This enables you to learn more quickly, which means faster experimentation, which means a better tuning cycle, which means better artwork!
Author(s) / creator(s) / reference(s)
Microsoft
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Quick tips to tune your GANs
Contributors
- Objectives
- Help you get started in developing and tuning GANs which generate high-resolution artwork
- Potential Use Cases
- image generation of high-resolution artwork
- Who is This For ?
- INTERMEDIATEPython developers with some experience with Deep Neural Nets and want to try their hands at generating artwork
Click on each of the following annotated items to see details.
WRITEUP 1. GAN - Ways to improve GAN performance
- What are the common ways to tune GANs?
10 minutes
WRITEUP 2. A couple of good ways to deal with limited datasets....
- What happens when I don't have enough data?
20 minutes
REPO 3. Use DeepSpeed
- What are the ways that I can parallelize and speed up my GANs' training?
10 minutes
WRITEUP 4. Use Dilations on your Discriminator to increase the receptive field size of your convolutional filters.
- What does increasing the dilation of your convolutional filters do?
10 minutes
Concepts Covered
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