Image Super-Resolution GANs
What you'll learn
- Create a generator architecture that upsamples an image by 4 times in each dimension
- Create a discriminator architecture that scores both realism and fidelity to the original image
- Modify custom written Keras layers to accept input images of any size without rebuilding the model
- Train the models on a Cloud TPU through Google CoLab
- Use the trained generator in a practical application to upsample your own images
- My "High Resolution Generative Adversarial Networks (GANs)" course
- Python experience
- Convolutional neural network experience
- Basic familiarity with TensorFlow 2.0 and Keras
We've all seen the gimmick in crime TV shows where the investigators manage to take a tiny patch of an image and magnify it with unrealistic clarity. Well today, Generative Adversarial Networks are making the impossible possible.
Dive into this course where I’ll show you how easily we can take the fundamentals from my High Resolution Generative Adversarial Networks course and build on this to accomplish this impressive feat known as Super-resolution. Not only will you be able to train a Generator to magnify an image to 4 times it’s original size (that’s 16 times the number of pixel!), but it will take relatively little effort on our end.
Just as in the first course, we’ll use Python and TensorFlow 2.0 along with Keras to build and train our convolutional neural networks. And since training our networks will require a ton of computational power, we’ll once again use Google CoLab to connect to a free Cloud TPU. This will allow us to complete the training in just a few days without spending anything on hardware!
If this sounds enticing, take a few minutes to watch the free preview of the “Results!” lesson. I have no doubt that you will come away impressed.
Who this course is for:
- Python + TensorFlow 2.0 developers who want to enlarge images with photorealistic detail and clarity
I'm a software engineer with a passion for machine learning.
I have over a decade of professional experience developing software for a large corporation but recently decided to dedicate myself full-time to machine learning research. I look forward to learning about exciting new topics, and in turn, teaching them here.
My long-term goal is to help push forward the state of the art in machine learning so that we can one day apply it our world's greatest problems particularly health and longevity.
I believe in Deep Mind's mission to "solve intelligence" and then use intelligence "to solve everything else".