Summary
The authors present a novel method for interpretable image classification by incorporating a vision transformer (ViT) into the prototypical neural network framework which provides case-based reasoning to neural network based image classifiers. They claim that most existing prototypical methods are convolutional neural network (CNN)-based, and those methods are limited by spatially rigid prototypes, thus failing to handle geometric variations of objects. Existing methods that try to handle this geometric variation either rely on a continuous latent space, which is not compatible with ViTs, or are other prototype-based ViT that fail to provide inherently interpretable explanations. Due to these existing problems, the author present ProtoViT with the following contributions:
Incorporates a ViT backbone that can adaptively learn interpretable prototypes that can handle geometric variation of different sizes.
They achieve the above with a greedy matching algorithm utilizing an adjacency mask and an adaptive slots mechanism.
They give empirical evaluation showing SOTA accuracy and a qualitative analysis showing the faithfulness and coherence of the learned prototype representations.
Strengths
Soundness:
The methods are clean and sound with ample ablation experiments, and it appears their approach can perform better than others (marginally) and have interpretable and coherent prototypes.
Presentation:
The paper was easy to follow with clear claims, ideas and methods. While some of the figures could be a bit cleaner (such as the boundaries and borders in Figure 4), and some notation seemed a bit odd, they communicated their ideas/methods well.
Contribution:
The paper gives a clean method for utilizing ViTs in the prototypical framework of deeplearning for interpretability and even incorporates existing methods for making prototypes more flexible via utilizing the approach from Deformable ProtoPNet. They incorporate a novel coherence loss that encourages sub prototypes to be similar to each other. In addition, this paper utilizes a greedy matching algorithm with an adaptive mask to learn geometrically local sub-prototypes. This method also allows for an adaptive number of sub-prototypes through the slot pruning mechanism.
Weaknesses
Soundness:
The lack of qualitative comparison with other ViT methods. I know the authors state that these other vision transformer methods do not project the learned prototypical features to the closest latent patches, but they still provide explanations. Could more be expanded on this and/or a figure showing this lack of reasoning/inherent interpretability?
- This is my biggest concern
Presentation:
In the section 3.4 for “Optimization of last layers” did you mean “... l-th class prototypes…” with a plural on the prototypes? This was unclear to me
Contribution:
However due to already preexisting ViT prototype methods (with a lack of comparison to them), the contribution this ViT makes compared to others is unclear.
Questions
Examples in the paper only show good outcomes. What do incorrect predictions look like, and does the explanation (prototypes) show a reason for why the model was incorrect?
Do the authors have additional comments about potential information leakage between image patches in the attention step occurring in the activation of the prototypes? I ask this in context of a latent patch containing more information regarding other patches around it contributing to the learned prototype. When we project on this prototype, we look at its spatial position and project to the real image, but it may not tell the whole story.
When determining the next sub-prototype, why do you only consider the last sub-prototype when creating the adaptive mask given radius r? Why not all the currently selected sub-prototypes? Wouldn’t you get more coverage and cohesive information if considering them all?
Why can’t prototypes be shared across classes? While I still think the prototypes learned are good, I think a limitation that wasn’t stated is the lack of across class prototype sharing.
I feel like the coherence loss could limit your overall prototype representation. If all sub-prototypes have to be similar, then wouldn’t that hurt a prototype representation that has distinct parts. I’m thinking of a potential example of three differently colored stripes being next to each other. If the sub-prototypes are each of different colors, wouldn’t they be very different; thus discarded due to this loss? This is even shown in E.3. I know that is where the adaptive mask could combat this problem, but I would like more discussion on this.
I’m curious how sensitive your method is to masking / perturbation. For example, if you mask out some or all the sub-prototypes for the top activated area in the test image, can the model still find the prototype elsewhere if it still exists in the image? A particular case I’m interested in is if a prototype is about the blue on a bird’s feathers, so you make that part that the model initially detected. Does the model still pick up the blue elsewhere?
Could you expand more on why you “believe that simply adding prototype layers to an architecture without well defined “cases” does not make the new architecture more interpretable.” In particular, what do you mean by ‘well defined cases’?
Limitations
The method lacks across class prototype sharing.
The coherence loss lack discussion on its limitation of making sub-prototypes being similar thus hindering diverse representation which may be important in a prototype.
The paper lacks comparison to other interpretable ViT methods