Abstract: In this study, we propose a novel architecture, the Quantum Pointwise Convolution, which incorporates pointwise convolution within a quantum neural network framework. Our approach leverages ...

In this section, I'd like to discuss potential issues with deep networks, specifically vanishing gradients. I will also be mentioning the possible solutions for this, residual connections and batch ...

This repository contains the hardware design, software references, and lab documentation for EE310 Lab 4. The project develops parameterizable depthwise, pointwise, and regular 2D convolution blocks ...

MobileNetで使われるDepthwise Separable Convolutionについて、簡単な例を用いて解説します。 Depthwise Separable Convolutionとは？ Depthwise Separable Convolutionは、通常の畳み込み演算を2つのステップに分解することで、計算量を大幅に減らす手法です。 1. 通常の畳み込み（例 ...

Traditional convolution is the foundation of convolutional neural networks (CNNs). It involves sliding a set of learnable filters (kernels) over the input data (e.g., an image) to generate feature ...

Research on image-inpainting tasks has mainly focused on enhancing performance by augmenting various stages and modules. However, this trend does not consider the increase in the number of model ...

Abstract: In overhead power system (OPS), the contact lines are suspended on the bearing cable through the droppers, and the structural height of the contact suspension is controlled by adjusting the ...