AdvisingNets: Learning to Distinguish Correct and Wrong Classifications
  via Nearest-Neighbor Explanations
Giang Nguyen Valerie Chen Anh Nguyen
Besides providing insights into how an image classifier makes its
predictions, nearest-neighbor examples also help humans make more accurate
decisions. Yet, leveraging this type of explanation to improve both human-AI
team accuracy and classifier's accuracy remains an open question. In this
paper, we aim to increase both types of accuracy by (1) comparing the input
image with post-hoc, nearest-neighbor explanations using a novel network
(AdvisingNet), and (2) employing a new reranking algorithm. Over different
baseline models, our method consistently improves the image classification
accuracy on CUB-200 and Cars-196 datasets. Interestingly, we also reach the
state-of-the-art human-AI team accuracy on CUB-200 where both humans and an
AdvisingNet make decisions on complementary subsets of images.