Neural networks make mistakes. The reason why a mistake is made often remains a mystery. It would be useful to have a method that can give an explanation that is intuitive to a user as to why an image is misclassified. In this paper we develop a method for explaining the mistakes of an image classification model by visually showing what must be added to an image such that it is correctly classified. Our work combines the fields of adversarial examples, generative modeling and a correction technique based on difference target propagation to create an technique that creates explanations of why an image is misclassified. In this paper we explain our method and demonstrate it on MNIST and CelebA. This approach could aid in demystifying neural networks for a user.
In this paper, the authors aim to better understand the classification of neural networks. The authors explore the latent space of a variational auto encoder and consider the perturbations of the latent space in order to obtain the correct classification. They evaluate their method on CelebA and MNIST datasets.
Pros:
 1) The paper explores an alternate methodology that uses perturbation in latent spaces to better understand neural networks 
2) It takes inspiration from adversarial examples and uses the explicit classifier loss to better perturb the $z$ in the latent space
3) The method is quite simple and captures the essence of the problem well
Cons:
The main drawback of the paper is it claims to understand working of neural networks, however, actually what the authors end up doing are perturbations of the encoded latent space. This would evidently not explain why a deep network generates misclassifications for instance understanding the failure modes of ResNet or DenseNet cannot be obtained through this method. Other drawbacks include:
1)     They do not show how their method would perform against standard adversarial attack techniques, since by explaining a neural network they should be able to guard against attacks, or at-least explain why they work well. 
2) The paper reports results on 2 datasets, out of which on 1 of them it does not perform well and gets stuck in a local minima therefore implying that it is not able to capture the diversity in the data well. 
3) The authors provide limited evaluation on a few attributes of CelebA. Extensive evaluation that would show on a larger scale with more attributes is not performed.
4) The authors also have claimed that the added parts should be interpretable and visible. However, the perturbations of the latent space would yield small $\epsilon$ variation in the image and it need not actually explain why the modification is yielding a correct classification, the same way an imperceptible adversarial attack yields a misclassification. Therefore there is no guarantee that the added parts would be interpretable. What would be more reasonable to claim would be that the latent transformations that yield correct classifications are projected into the original image space. Some of these yield interpretations that are semantically meaningful and some of these do not yield semantically meaningful interpretations.
5)  Solving mis-classification does not seem to equate with explaining the neural network, but rather only suggest where it makes mistakes. That is not equal to an explanation about how it is making a classification decision. That would rather be done by using the same input and perturbing the weights of the classifier network. 
In conclusion, the paper in its current form provides a direction in terms of using latent space exploration to understand classification errors and corrections to them in terms of perturbations of the latent space. However, these are not conclusive yet and actually verifying this would need a more thorough evaluation.