Basically, we want to create a system that can accurately identify objects (like buildings or roads) in satellite images. But instead of using traditional methods like training the model from scratch on a huge dataset, we’re going to use something called “knowledge distillation” to make it more efficient and accurate.

Here’s how it works: first, we have a big, fancy model that can already identify objects pretty well (let’s call this our teacher). We feed some satellite images into the teacher, and it spits out predictions for what’s in each image. But instead of just using those predictions as-is, we want to teach another smaller, simpler model (our student) how to do the same thing.

So, we take all the predictions that our fancy teacher made, and we use them to train our student model. The idea is that by learning from a more experienced model, our student will be able to make better predictions on its own. And because it’s smaller and simpler than the teacher, it should be faster and more efficient too!

But here’s where things get interesting: instead of just using static knowledge (like all those pre-trained weights from the fancy teacher), we want our student model to learn dynamically as well. That means that it can adapt to new situations and improve over time, even if it doesn’t have access to a huge dataset like the teacher does.

So how do we make this happen? Well, instead of just using static weights from the fancy teacher, we use something called “dynamic knowledge distillation” (hence the title!). This involves feeding our student model not only the predictions that the teacher made, but also some information about how confident those predictions were.

For example, if the teacher was really sure that there’s a building in an image, we might give our student model more weight to that prediction than if it wasn’t so sure. And if the teacher was less confident (maybe because of some noise or other distortion), we might adjust the weights accordingly as well.

The result is a system that can learn from both static and dynamic knowledge, making it more efficient and accurate than traditional methods. Plus, since our student model is smaller and simpler than the fancy teacher, it should be faster to train and run too!