  We propose Guided Zoom, an approach that utilizes spatial grounding of a
model's decision to make more informed predictions. It does so by making sure
the model has "the right reasons" for a prediction, defined as reasons that are
coherent with those used to make similar correct decisions at training time.
The reason/evidence upon which a deep convolutional neural network makes a
prediction is defined to be the spatial grounding, in the pixel space, for a
specific class conditional probability in the model output. Guided Zoom
examines how reasonable such evidence is for each of the top-k predicted
classes, rather than solely trusting the top-1 prediction. We show that Guided
Zoom improves the classification accuracy of a deep convolutional neural
network model and obtains state-of-the-art results on three fine-grained
classification benchmark datasets.
