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 ...

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 ...

Abstract: Fast time-domain algorithms have been developed in signal processing applications to reduce the multiplication complexity. For example, fast convolution structures using Cook-Toom and ...

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 ...

In modern industrial production, accurate and efficient detection of strip-steel surface defects is critical for maintaining product quality and minimizing economic losses. With the rapid advancement ...

Coronavirus 2019 (COVID-19) is a new acute respiratory disease that has spread rapidly throughout the world. In this paper, a lightweight convolutional neural network (CNN) model named multi-scale ...

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 ...

「通常の畳み込み（Standard Convolution）を、『空間方向の処理』と『チャンネル方向の処理』の2つのステップに分解することで、計算量とパラメータ数を劇的に削減する手法」のことです。 仕組みの核心: 通常の畳み込みは、「空間的な特徴（縦×横）」と ...

Standard convolutional layers are fundamental in neural networks, particularly for image-based tasks. A standard convolutional layer converts an input feature map with multiple channels (depth) into ...