  Computer vision has flourished in recent years thanks to Deep Learning
advancements, fast and scalable hardware solutions and large availability of
structured image data. Convolutional Neural Networks trained on supervised
tasks with backpropagation learn to extract meaningful representations from raw
pixels automatically, and surpass shallow methods in image understanding.
Though convenient, data-driven feature learning is prone to dataset bias: a
network learns its parameters from training signals alone, and will usually
perform poorly if train and test distribution differ. To alleviate this
problem, research on Domain Generalization (DG), Domain Adaptation (DA) and
their variations is increasing. This thesis contributes to these research
topics by presenting novel and effective ways to solve the dataset bias problem
in its various settings. We propose new frameworks for Domain Generalization
and Domain Adaptation which make use of feature aggregation strategies and
visual transformations via data-augmentation and multi-task integration of
self-supervision. We also design an algorithm that adapts an object detection
model to any out of distribution sample at test time. With through
experimentation, we show how our proposed solutions outperform competitive
state-of-the-art approaches in established DG and DA benchmarks.
