We are conducting various research projects to provide appropriate constraints and assistance to the architectures and optimizations of deep learning.
Recent advances in Neural Architecture Search (NAS) have made it possible to automatically design efficient architectures for image classification tasks. Convolutional Neural Networks (CNNs) are commonly used in image classification, which rely primarily on convolution and pooling operations. While traditional NAS methods have focused on selecting among these operations, recent work has shown that incorporating attention mechanisms into CNNs can increase representational power, improving accuracy while limiting the growth in parameters. In this study, we propose a method for automatically designing CNNs that integrate attention mechanisms.
Deep convolutional neural networks (CNNs) with a very large number of parameters have achieved remarkable success in image processing. However, an excessively large number of parameters is increases the risk of overfitting. To mitigate this, various data augmentation methods have been proposed, such as flipping, cropping, scaling, and color transformations. Building on these techniques, our study introduces a new technique in which we randomly crop four different images and patch them together to form a new training sample, thereby achieving even higher accuracy in image processing tasks.
Reinforcement learning (RL) agents are capable of solving a wide range of tasks, including the board game Go, autonomous driving, and video games. Although RL trains agents to maximize rewards, practical applications demand considerations beyond pure performance. For instance, an agent that is too strong can diminish user enjoyment in video games, while in autonomous driving, excessive acceleration and deceleration may cause passenger anxiety. As a result, there is growing interest in designing agents with more human-like behavior. Imitation learning, which trains agents on expert human policies, can yield human-like actions but cannot surpass the expert's performance. In this study, we propose a method that integrates reinforcement learning with imitation learning, thereby combining the strengths of both approaches. We applied our model to Atari games and the driving simulator TORCS, and experimental evaluation demonstrated that our method outperforms imitation-only agents while exhibiting human-like behavior compared to RL-only agent. This research was conducted as a joint research project with Equos Research Co., Ltd.
Marine weather observations are essential for safe navigation, and in Japan, even commercial vessels are required to report their findings to the Japan Meteorological Agency. However, conventional weather instruments cannot determine cloud type or cloud cover, requiring observers to rely on visual inspection. Existing approaches developed in other countries are not fully adapted to Japanese weather conditions or reporting standards, highlighting the need for a Japan-specific system. In this study, we developed a device capable of capturing full-sky images, installed it on vessels to collect medium-scale datasets, and labeled cloud types and conditions in the lower, middle, and upper layers of the atmosphere. After training a deep convolutional neural network on these datasets, we confirmed that both cloud type and condition classification achieved accuracies above 0.9. This work was conducted as a joint research project with SKY Perfect JSAT Corporation, Banyan Partners, Kobe Digital Labo Inc., and Professor Osawa of the Graduate School of Maritime Sciences at Kobe University.
Part of the research results has been released as the iOS and Android app "Kumolog." It can be downloaded from the App Store and Google Play. It has also been featured in the Kobe University Press Release, NHK WEB NEWS, Asahi Shimbun, and Nikkan Kogyo Shimbun.
