61 research outputs found
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
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