  In this study, the effects of different class labels created as a result of
multiple conceptual meanings on localization using Weakly Supervised Learning
presented on Car Dataset. In addition, the generated labels are included in the
comparison, and the solution turned into Unsupervised Learning. This paper
investigates multiple setups for car localization in the images with other
approaches rather than Supervised Learning. To predict localization labels,
Class Activation Mapping (CAM) is implemented and from the results, the
bounding boxes are extracted by using morphological edge detection. Besides the
original class labels, generated class labels also employed to train CAM on
which turn to a solution to Unsupervised Learning example. In the experiments,
we first analyze the effects of class labels in Weakly Supervised localization
on the Compcars dataset. We then show that the proposed Unsupervised approach
outperforms the Weakly Supervised method in this particular dataset by
approximately %6.
