How can techniques like transfer learning, meta-learning, and synthetic data generation help in these scenarios?

The basic CNN architecture consists of five main layers: input, convolutional, activation, pooling, and fully connected. Each layer plays a specific role in ... Feb 8, 2022 ... ... Machine Learning, Stockholm, Sweden, PMLR 80 1–19 (2018) ... Random CNN structure: tool to increase generalization ability in deep learning. ... CNN deep feature extraction with machine learning models to classify variety of food image datasets. Date of Award, Oct 2018. Original language, English. Jun 27, 2017 ... The convolution in a CNN is going to be the kernel g of which we will learn some weights in order to extract the information we need and then perhaps apply an ... A convolutional neural network (CNN/ConvNet) is a class of deep neural networks, most commonly applied to analyze visual imagery. Layer-Wise Data-Free CNN Compression ... We present an efficient method for compressing a trained neural network without using any data. Our data-free method ... A regional wind wave prediction surrogate model based on CNN deep learning network ... In recent years, machine learning methods have gradually been ... Feb 20, 2020 ... LeNet-5. LeNet-5 architecture is perhaps the most widely known CNN architecture. It was created by Yann LeCun in 1998 and widely used for ... Complex problems can be solved and objects can be recognized in images based on the Google TensorFlow ™ library - a widely used deep learning framework. Based ... Mar 9, 2024 ... A 1-dimensional convolutional neural network (1D CNN) model is developed to process deep learning of raw impedance signals for smart ...