In the project, we have developed various techniques that allow training a neural network using even just a single example and still getting remarkable results.
We also have shown that one can train a neural network for one simple task (e.g. image denoising) and still use it for various other tasks such as increasing the resolution of the image, filling missing parts in it, or removing blur from it.
Our work also extended beyond the standard data that is used with neural networks. This includes:
1. Develop novel neural network strategies for processing, generation and editing of 3D shapes. The following video explains one of our approaches
https://youtu.be/3cKGSV-VUVI(odnośnik otworzy się w nowym oknie)2. Detecting exoplanets in the outer space
3. Processing of radar data.
4. We have shown how one can automatically design optical systems using neural networks. This can bring the ability to new areas such as achieving all in focus imaging and depth reconstruction from only a single lens.
5. We have developed novel image processing techniques.
6. We proposed novel strategies for self-supervised and few-shot learning
7. We improved the understanding of adversarial examples and proposed new techniques to mitigate them
8. We proposed novel theoretical understanding of neural networks based on signal processing based tools, where sparsity is a notable example.
All these results brought deep learning to areas in which it was less used before.
