Disclaimer: I would not write code like this today :3
In computer vision, neural networks have been successfully employed to solve multiple tasks in parallel in one model. The joint exploitation of related objectives can improve the performance of each individual task. The architecture of these multi task models can be more complex since the networks branch into the atomic tasks at a certain depth. For this reason the process of designing such architectures often involves a lot of time consuming trial-and-error. Therefore a more systematic taxonomy is desirable to replace this experimental process. For constructing this taxonomy it is necessary to have an understanding of the latent information learned by specific layers of single task models. This work uses convolutional variational autoencoders to produce latent representations of aerial images which are analyzed to understand the inner workings of the models. The method relies on testing whether or not learned clusters can be attributed to different high level input features like topographic classes common for the field of remote sensing. Visualizations are produced to gain insight and an understanding of the information captured in the latent space of the variational autoencoders. Moreover, it is observed how different architectural choices affect the reconstructions and the latent space. Code to reproduce the experiments is publicly available here: https://github.com/HannesStaerk/bachelorThesis.
Every script like Kernel3adjusted2x2x256.py contains an architecture with an adjustable
coding size for the latent vector. The a data_source_dir with 128x128 images
has to be specified and functions to train, to make predictions and generations and to
create the t-SNE and PCA visualizations can be called.
In utils.py there is a function to split and resize images.
This can be used to split or risize the 1024x1024 images given by the dataset into the 128x128
images that the models take as input.
The Data can be downloaded here http://www.grss-ieee.org/community/technical-committees/data-fusion/2019-ieee-grss-data-fusion-contest-data/