What are some interesting open research questions in the field of CNNs that students and researchers can explore?

Current approaches to object recognition make essential use of machine learning methods. To im- prove their performance, we can collect larger datasets, learn ... Aug 5, 2022 ... An integrated and hybrid approach based on deep convolutional neural network and machine learning classifiers is proposed for the accurate segmentation and ... Feb 1, 2023 ... Convolutional Neural Networks (CNNs) is a powerful deep learning architecture well-suited to image classification and object recognition tasks. Oct 2, 2023 ... ... machine learning techniques to overcome the non ... CNN's learned associations related to ejection events in wall-bounded turbulent flows. Explore and run machine learning code with Kaggle Notebooks | Using data from Digit Recognizer. Mar 25, 2023 ... In this paper, we compared and explored the two methodologies on the DEMoS dataset consisting of 8869 audio files of 58 speakers in different emotional states. Nov 14, 2023 ... A Convolutional Neural Network (CNN), also known as ConvNet, is a specialized type of deep learning algorithm mainly designed for tasks that necessitate object ... The objective of this paper is using deep learning to uncover higher-level representational features, to clearly explain transfer learning, to provide current ... Jun 10, 2020 ... ... machine learning WorldView-2 results,. (d) a Sentinel-2 image of Study Site 1, (e) CNN deep learning Sentinel results, and (f) RF machine. Feb 8, 2022 ... ... Machine Learning, Stockholm, Sweden, PMLR 80 1–19 (2018) ... Random CNN structure: tool to increase generalization ability in deep learning.