This Looks Like That: Deep Learning for Interpretable Image Recognition

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Presented by

Nouha Chatti


The motivation behind this paper is to introduce a new deep learning network architecture capable of reasoning in a humanly understandable way dealing with classification tasks. The idea is to perform these tasks by defining a form of interpretability when processing the images. The method suggested in this paper consists in dissecting parts of the input images and comparing them to prototypical parts of training images of a given class: Thus the expression this looks like that. In fact, this solution adds a transparency advantage to deep neural networks and allows the user to understand the actual process of decision making. It can intervene in many crucial problems that require understanding the actions that led to a particular output of the model. There are many fields that already rely on this case-based reasoning especially in the medical domain where diagnosis using X-ray scans is based on comparing these latter to other prototypical scans.

Previous Work

Interpretability in Deep neural networks has been a long-sought goal and seems to attract more and more attention recently. The opacity present in neural networks that leaves the user unaware of the exact process of how model makes predictions has inspired many studies where their ultimate goal was to reach a certain transparency. As a matter of fact, there already exists posthoc interpretability methods that analyze a performance of a trained CNN. Although this type of analysis do not explain the reasoning process of how a network actually makes its decisions during classification but are rather created after this phase. There are also attention-based models that determines parts of the input they are looking at but without associating them to prototypes.

Network Architecture

Figure 1 : Prototypical Part Network Architecture

The above figure represents the ProtoPNet architecture. The first layers of propPNet consist of commonly used convolutional layers f. (their parameters are denoted wconv). The layers used in this study are from the following known models VGG-16, VGG-19, ResNet-34, ResNet-152, DenseNet-121, and DenseNet-161 previously pretrained on ImageNet.They are also followed by two additional 1 × 1 convolutional layers. A layer called prototype gp, a fully connected layer h with weight wh and no bias that returns the output prediction using a softmax function unlike all the rest of the layers that use ReLU as activation function. This network takes in an image x propagates it through the convolutional layers (f of shape H x W x D) where features are extracted and learns the porotypes P of shape (1 x 1 x D). the number of prototypes mk is pre-defined for each class k (10 per class in this study) Each will be used to represent a pattern in a patch of the conv output, corresponding to some prototypical image patch in the original pixel space. So given an output z = f(x), the prototype unit gpj in the prototype layer gp computes the squared L2 distances between the j-th prototype pj and all patches of z that have the same shape as pj and returns similarity scores. These scores values indicates the presence of the prototypical part In the image all while preserving the spatial relation of z. It is possible to upsample it to the original size in order to obtain a heat map with the different part that are most similar to the compared prototypes. The scores given by each unit are produced using max pooling to obtain a single score of how strong a prototypical pattern is present in the specific patch of the input, multiplied by the weight matrix wh in h to produce the output logits as shown in the figure.