Learning from AI: An Interactive Learning Method Using a DNN Model Incorporating Expert Knowledge as a Teacher

Visual explanation is an approach for visualizing the grounds of judgment by deep learning, and it is possible to visually interpret the grounds of a judgment for a certain input by visualizing an attention map. As for deep-learning models that output erroneous decision-making grounds, a method that incorporates expert human knowledge in the model via an attention map in a manner that improves explanatory power and recognition accuracy is proposed. In this study, based on a deep-learning model that incorporates the knowledge of experts, a method by which a learner "learns from AI" the grounds for its decisions is proposed. An "attention branch network" (ABN), which has been fine-tuned with attention maps modified by experts, is prepared as a teacher. By using an interactive editing tool for the fine-tuned ABN and attention maps, the learner learns by editing the attention maps and changing the inference results. By repeatedly editing the attention maps and making inferences so that the correct recognition results are output, the learner can acquire the grounds for the expert's judgments embedded in the ABN. The results of an evaluation experiment with subjects show that learning using the proposed method is more efficient than the conventional method.Comment: 12 pages, 5 figure

Masking and Mixing Adversarial Training

While convolutional neural networks (CNNs) have achieved excellent performances in various computer vision tasks, they often misclassify with malicious samples, a.k.a. adversarial examples. Adversarial training is a popular and straightforward technique to defend against the threat of adversarial examples. Unfortunately, CNNs must sacrifice the accuracy of standard samples to improve robustness against adversarial examples when adversarial training is used. In this work, we propose Masking and Mixing Adversarial Training (M2AT) to mitigate the trade-off between accuracy and robustness. We focus on creating diverse adversarial examples during training. Specifically, our approach consists of two processes: 1) masking a perturbation with a binary mask and 2) mixing two partially perturbed images. Experimental results on CIFAR-10 dataset demonstrate that our method achieves better robustness against several adversarial attacks than previous methods

Normalization of VIIRS DNB images for improved estimation of socioeconomic indicators

Monthly Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) composite data are widely used in research, such as estimations of socioeconomic parameters. However, some surface conditions affect the VIIRS DNB radiance, which may create some estimation bias in certain regions. In this paper, we propose a novel normalization algorithm for VIIRS DNB monthly composite data. The aim is to normalize VIIRS radiance, collected under different surface conditions, to a reference point, so that the bias is reduced. The algorithm is based on the utilization of stable lit pixels as a reference and a nonlinear regression algorithm, to match un-normalized data to the reference data. Experimental results show that the algorithm could improve correlation (R2) between the total sum of nightlights (TOL), electric power consumption (EPC), and gross domestic product (GDP) at both a global and local scale. The algorithm could significantly diminish the seasonal component of un-normalized nightlights radiance caused by snow. The intensified nightlights radiance in sandy regions could also be reduced to a more reasonable range in comparison with other regions. Visual inspection shows that the brightness of snow-affected and sandy regions was strongly reduced after undergoing normalization