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.
翻译:AI最近的发展使得它变得无处不在,每个行业都在试图采用某种形式的智能处理数据的方法。尽管在这一领域取得了许多进展,但该行业尚未充分利用AI的全部能力。由于对此类自主系统缺乏信任,涉及某些风险因素的工业仍然对AI的使用保持谨慎。今天的AI在很多方面可能非常不错,但在推理上却非常糟糕,而AI的这种行为可能导致灾难性的结果。自动撞入一个人或被困在树上的无人驾驶飞机是AI决定导致灾难性结果的几个例子。AI决定导致灾难性结果的几个例子。为了对AI的学习能力进行深入了解和提出解释,我们将试图分析损失功能的运行情况。对于我们来说,我们将使用两种损失功能,即普遍损失功能,例如Binary交叉季节性或BCE和专门的损失功能,以及Dice损失或焦点损失等专门的损失功能。通过一系列实验,我们将确定将不同的损失功能合并起来是否比使用单一的损失率更好,如果是的话,那么,那么它背后的原因是什么。为了确定普遍损失和专门损失的精确度之间的差别,我们将会用一些不同的模型来比较。