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

PALF: Pre-Annotation and Camera-LiDAR Late Fusion for the Easy Annotation of Point Clouds

3D object detection has become indispensable in the field of autonomous driving. To date, gratifying breakthroughs have been recorded in 3D object detection research, attributed to deep learning. However, deep learning algorithms are data-driven and require large amounts of annotated point cloud data for training and evaluation. Unlike 2D image labels, annotating point cloud data is difficult due to the limitations of sparsity, irregularity, and low resolution, which requires more manual work, and the annotation efficiency is much lower than 2D image.Therefore, we propose an annotation algorithm for point cloud data, which is pre-annotation and camera-LiDAR late fusion algorithm to easily and accurately annotate. The contributions of this study are as follows. We propose (1) a pre-annotation algorithm that employs 3D object detection and auto fitting for the easy annotation of point clouds, (2) a camera-LiDAR late fusion algorithm using 2D and 3D results for easily error checking, which helps annotators easily identify missing objects, and (3) a point cloud annotation evaluation pipeline to evaluate our experiments. The experimental results show that the proposed algorithm improves the annotating speed by 6.5 times and the annotation quality in terms of the 3D Intersection over Union and precision by 8.2 points and 5.6 points, respectively; additionally, the miss rate is reduced by 31.9 points

Robust Self-Catalytic Reactor for CO2 Methanation Fabricated by Metal 3D Printing and Selective Electrochemical Dissolution

The methanation of CO2 has been actively pursued as a practical approach to mitigating global climate change. However, the complexity of the catalyst development process has hindered the development of new catalysts for CO2 methanation; as a result, few catalysts are commercially available. Herein, a multifunctional self-catalytic reactor (SCR) is prepared via metal 3D printing and selective electrochemical dissolution as a method to not only simplify the catalyst development process but also fabricate active catalysts for CO2 methanation. The combination of metal 3D printing and selective electrochemical dissolution is demonstrated as a feasible method to prepare active catalysts for the methanation of CO2 in a short time. In addition, the use of an electrochemical method enables the formation of galvanic cells on the SCR; these cells continuously generate active sites via self-dissolution during a simple refresh process, resulting in high reusability of the SCR. The proposed method represents a new facile technique to fabricate highly reusable catalysts that exhibit superior performance for CO2 methanation, and the results provide a guideline for preparing metal 3D-printed catalysts that will satisfy industrial demand.Kim H.J., Mori K., Nakano T., et al. Robust Self-Catalytic Reactor for CO2 Methanation Fabricated by Metal 3D Printing and Selective Electrochemical Dissolution. Advanced Functional Materials , (2023); https://doi.org/10.1002/adfm.202303994