  Unsupervised representation learning aims at finding methods that learn
representations from data without annotation-based signals. Abstaining from
annotations not only leads to economic benefits but may - and to some extent
already does - result in advantages regarding the representation's structure,
robustness, and generalizability to different tasks. In the long run,
unsupervised methods are expected to surpass their supervised counterparts due
to the reduction of human intervention and the inherently more general setup
that does not bias the optimization towards an objective originating from
specific annotation-based signals. While major advantages of unsupervised
representation learning have been recently observed in natural language
processing, supervised methods still dominate in vision domains for most tasks.
In this dissertation, we contribute to the field of unsupervised (visual)
representation learning from three perspectives: (i) Learning representations:
We design unsupervised, backpropagation-free Convolutional Self-Organizing
Neural Networks (CSNNs) that utilize self-organization- and Hebbian-based
learning rules to learn convolutional kernels and masks to achieve deeper
backpropagation-free models. (ii) Evaluating representations: We build upon the
widely used (non-)linear evaluation protocol to define pretext- and
target-objective-independent metrics for measuring and investigating the
objective function mismatch between various unsupervised pretext tasks and
target tasks. (iii) Transferring representations: We contribute CARLANE, the
first 3-way sim-to-real domain adaptation benchmark for 2D lane detection, and
a method based on prototypical self-supervised learning. Finally, we contribute
a content-consistent unpaired image-to-image translation method that utilizes
masks, global and local discriminators, and similarity sampling to mitigate
content inconsistencies.
