This project explores image classification using CNNs on the CIFAR-10 dataset. It optimizes CNN architecture for high accuracy, showcasing its efficacy in diverse real-world applications. This project ...

This project focuses on image classification using Convolutional Neural Networks (CNNs) to classify images from the CIFAR-10 dataset. The model is optimized to achieve higher accuracy using data ...

Paper Summary: U-Net: Convolutional Networks for Biomedical Image Segmentation, MICCAI 2015 Olaf Ronneberger, Philipp Fischer, and Thomas Brox [DOI] In this paper, the authors proposed a fully ...

In the field of image classification, convolutional neural networks are widely used because of its efficient feature extraction ability. However, some of the features extracted by convolutional neural ...

In this paper, three types of convolution operations in convolutional neural networks (CNNs) are studied including regular convolution, separable convolution and group convolution. For regular ...