Unpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing Software

This paper tackles unpaired image enhancement, a task of learning a mapping function which transforms input images into enhanced images in the absence of input-output image pairs. Our method is based on generative adversarial networks (GANs), but instead of simply generating images with a neural network, we enhance images utilizing image editing software such as Adobe Photoshop for the following three benefits: enhanced images have no artifacts, the same enhancement can be applied to larger images, and the enhancement is interpretable. To incorporate image editing software into a GAN, we propose a reinforcement learning framework where the generator works as the agent that selects the software's parameters and is rewarded when it fools the discriminator. Our framework can use high-quality non-differentiable filters present in image editing software, which enables image enhancement with high performance. We apply the proposed method to two unpaired image enhancement tasks: photo enhancement and face beautification. Our experimental results demonstrate that the proposed method achieves better performance, compared to the performances of the state-of-the-art methods based on unpaired learning.Comment: Accepted to AAAI 202

Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation

A significant challenge in the field of quantum machine learning (QML) is to establish applications of quantum computation to accelerate common tasks in machine learning such as those for neural networks. Ridgelet transform has been a fundamental mathematical tool in the theoretical studies of neural networks, but the practical applicability of ridgelet transform to conducting learning tasks was limited since its numerical implementation by conventional classical computation requires an exponential runtime $\exp(O(D))$ as data dimension $D$ increases. To address this problem, we develop a quantum ridgelet transform (QRT), which implements the ridgelet transform of a quantum state within a linear runtime $O(D)$ of quantum computation. As an application, we also show that one can use QRT as a fundamental subroutine for QML to efficiently find a sparse trainable subnetwork of large shallow wide neural networks without conducting large-scale optimization of the original network. This application discovers an efficient way in this regime to demonstrate the lottery ticket hypothesis on finding such a sparse trainable neural network. These results open an avenue of QML for accelerating learning tasks with commonly used classical neural networks.Comment: 27 pages, 4 figure

Self-Play Reinforcement Learning for Fast Image Retargeting

In this study, we address image retargeting, which is a task that adjusts input images to arbitrary sizes. In one of the best-performing methods called MULTIOP, multiple retargeting operators were combined and retargeted images at each stage were generated to find the optimal sequence of operators that minimized the distance between original and retargeted images. The limitation of this method is in its tremendous processing time, which severely prohibits its practical use. Therefore, the purpose of this study is to find the optimal combination of operators within a reasonable processing time; we propose a method of predicting the optimal operator for each step using a reinforcement learning agent. The technical contributions of this study are as follows. Firstly, we propose a reward based on self-play, which will be insensitive to the large variance in the content-dependent distance measured in MULTIOP. Secondly, we propose to dynamically change the loss weight for each action to prevent the algorithm from falling into a local optimum and from choosing only the most frequently used operator in its training. Our experiments showed that we achieved multi-operator image retargeting with less processing time by three orders of magnitude and the same quality as the original multi-operator-based method, which was the best-performing algorithm in retargeting tasks.Comment: Accepted to ACM Multimedia 202

THEORETICAL ESTIMATION OF MECHANICAL PROPERTIES OF PLYWOOD-SHEATHED SHEAR WALL WITH COMBINED USE OF ADHESIVE TAPE AND WOOD DOWELS

Shear walls often function as elements that provide resistance to horizontal external forces exerted on wooden frames. Many shear walls with superior strength performance have been developed for this purpose. Amidst this backdrop, we have attempted to develop a shear wall that, in addition to strength performance, decreases the time and labor required for disposal. More specifically, the authors proposed a novel “metalless” shear wall: a shear wall in which industrial double-sided adhesive tape is used to attach plywood to the framework. Also, wood dowels are used as supplementary connectors with the aim of enhancing strength performance. Unlike conventional shear walls that use nails and metal fixtures, separation at the time of disposal is unnecessary, and therefore, disposal time and labor of the wall are anticipated to be significantly decreased. Thus, this study involved demonstrating and verifying a methodof theoretical analysis for the mechanical performance of these kinds of shear walls toward in-plane shear force. Specifically, this study derived a method to estimate the mechanical behavior (load-deformation angle relationship) of plywood-sheathed shear walls based on shear performance obtained from double shear tests of joint specimens with the combined use of adhesive tape and wood dowels. Also, the validity of the method was experimentally verified. The results showed that the method proposed in this study was able to estimate the mechanical behavior and mechanical properties of the newly proposed shear wall, and the validity of the method was confirmed

Electron spin phase relaxation of phosphorus donors in nuclear spin enriched silicon

We report a pulsed EPR study of the phase relaxation of electron spins bound to phosphorus donors in isotopically purified 29^Si and natural abundance Si single crystals measured at 8 K.Comment: 5 pages, 3 figure