  Recent developments in AI have made it ubiquitous, every industry is trying
to adopt some form of intelligent processing of their data. Despite so many
advances in the field, AIs full capability is yet to be exploited by the
industry. Industries that involve some risk factors still remain cautious about
the usage of AI due to the lack of trust in such autonomous systems.
Present-day AI might be very good in a lot of things but it is very bad in
reasoning and this behavior of AI can lead to catastrophic results. Autonomous
cars crashing into a person or a drone getting stuck in a tree are a few
examples where AI decisions lead to catastrophic results. To develop insight
and generate an explanation about the learning capability of AI, we will try to
analyze the working of loss functions. For our case, we will use two sets of
loss functions, generalized loss functions like Binary cross-entropy or BCE and
specialized loss functions like Dice loss or focal loss. Through a series of
experiments, we will establish whether combining different loss functions is
better than using a single loss function and if yes, then what is the reason
behind it. In order to establish the difference between generalized loss and
specialized losses, we will train several models using the above-mentioned
losses and then compare their robustness on adversarial examples. In
particular, we will look at how fast the accuracy of different models decreases
when we change the pixels corresponding to the most salient gradients.
