Deep Neural Networks have achieved remarkable progress in data analysis in a broad range of topics such as object recognition, machine translation and medical diagnostics.
![](https://hci.iwr.uni-heidelberg.de/vislearn/wp-content/uploads/2019/09/bird-1024x255.png)
An invertible neural net (INN) was trained to add colour to grayscale images. Which one is the original image? Answer at the bottom of the page. Image adapted from: [1]
![](https://hci.iwr.uni-heidelberg.de/vislearn/wp-content/uploads/2019/09/street-segmentation-1024x515.jpg)
Critical application for neural networks: Decide where a car can drive or not. Confidence estimation is critical to make the right decisions. Image adapted from: [2]
- How can we design deep networks under given constraints?
- (How) Can we be confident in the answers given by a network?
- Can we explain the behaviour of a network?
The questions are tackled using the new unconventional framework of transport theory: We treat the input and output of a network as probability distributions. This enables an easier mathematical and algorithmical approach. It is realised in the form of Invertible Neural Networks (INNs).
Literature
[1] L. Ardizzone, J. Kruse, C. Lüth, C. Rother, and U. Köthe. Guided image generation with conditional invertible neural networks. arXiv:1907.02392, 2019.
[2] Kendall, A., and Gal, Y.. What uncertainties do we need in bayesian deep learning for computer vision?. In Advances in neural information processing systems 2017 (pp. 5574-5584).
The original bird is the one all the way to the right.