| dc.description.abstract | Traditional supervised learning methods depend heavily on labeled data, which is both costly and
time-intensive to acquire. Self-supervised learning approaches present a promising alternative to
supervised learning, enabling the utilization of unlabeled data. Thus, this research aims to build
an advanced semi-supervised semantic segmentation model that strikes a balance between selfsupervised and fully supervised paradigms for visual perception applications in an autonomous
driving environment.
In this direction, the thesis is structured into three distinct phases, beginning with self-supervised
image classification and progressing toward bi-level image segmentation, ultimately culminating
in the development of an advanced semantic segmentation model. Initially, this research employs
a simple contrastive learning framework (SimCLR) to classify medical images, specifically focusing on monkeypox diagnosis from skin lesion images, while integrating a federated learning (FL)
framework to ensure data privacy. Monkeypox classification is a simple binary classification task
and the dataset found for this problem, in this thesis, is very manageable on the computational resources that were available at the onset of this research. It paved the way to grasp non-supervised
learning basics and explore how they differ from traditional supervised learning methods.
The subsequent phase involves the development of an efficient convolutional neural network
(CNN) with an attention mechanism, applied to the bi-level segmentation task of road pavement
crack detection. Similar to the Monkeypox classification, this is also a binary classification task,
but at pixel-level, i.e., it is a two-way semantic segmentation problem. Hence, the number of
samples found in the relevant datasets is once again manageable on the computational resources
available during the research. [...] | en_US |
