  Weakly Supervised Object Localization is challenging because of the lack of
bounding box annotations. Previous works tend to generate a class activation
map i.e CAM to localize the object. Unfortunately, the network activates only
the features that discriminate the object and does not activate the whole
object. Some methods tend to remove some parts of the object to force the CNN
to detect other features, whereas, others change the network structure to
generate multiple CAMs from different levels of the model. In this present
article, we propose to take advantage of the generalization ability of the
network and train the model using clean examples and adversarial examples to
localize the whole object. Adversarial examples are typically used to train
robust models and are images where a perturbation is added. To get a good
classification accuracy, the CNN trained with adversarial examples is forced to
detect more features that discriminate the object. We futher propose to apply
the shannon entropy on the CAMs generated by the network to guide it during
training. Our method does not erase any part of the image neither does it
change the network architecure and extensive experiments show that our Entropy
Guided Adversarial model (EGA model) improved performance on state of the arts
benchmarks for both localization and classification accuracy.
