From the course: Introduction to Generative Adversarial Networks (GANs)
Join today to access over 24,700 courses taught by industry experts.
Final model output
From the course: Introduction to Generative Adversarial Networks (GANs)
Final model output
- [Presenter] We've spoken about the architecture but what exactly is the final structure of the output? Once we've gone through the entire flow, all we really need is that generator model afterwards. So the discriminator has definitely done its job and we can save those weights and use it for the future. But what we actually just need are the weights of the generator model because if the model has been trained properly or if the system has been trained properly, it's weights should be quite useful and quite generative. The application we can then use is we can start generating other images from different seeds or even other different sources. If we wanted to, we could keep both models and start retraining on new data sets. So the models can be improved technically by taking in new data and giving it more breadth. So by that I mean the ability to generate new types of images. We could also change the…
Practice while you learn with exercise files
Download the files the instructor uses to teach the course. Follow along and learn by watching, listening and practicing.
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.