What are the common challenges in replicating deep learning experiments?

We have proposed a deep learning method called BCM-SegNet, an optimized Mask R-CNN-based algorithm for segmenting BSE images of bronze casting moulds and cores. A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep ... Jan 30, 2023 ... Abstract. In this study, the leaves are classified by various Machine Learning (ML) and Deep Learning (DL) based Convolutional Neural ... This theoretical spotlights on creating iris distinguishing proof frameworks using machine learning and CNN-based image processing. ... CNN Based Image Processing ... (Not recommended) Train R-CNN deep learning object detector. collapse all in ... This function requires that you have Deep Learning Toolbox™ and Statistics and ... Abstract. We present Point-Voxel CNN (PVCNN) for efficient, fast 3D deep learning. Previous work processes 3D data using either voxel-based or point-based NN ... Jun 27, 2024 ... ... machine learning by regression-based CNN. As a result, the lowest nonlinearity of 0.3% with precision as high as 98.7 is given in our PSD as ... A convolutional neural network (CNN or ConvNet) is a network architecture for deep learning that learns directly from data. ... machine learning model combining of CNN-LSTM-RF for time series forecasting of Solar Power Generation. Contact information for Technical Support and Student ... Apr 12, 2025 ... CNN in deep learning excels at image classification, which involves sorting images into predefined categories. They can effectively identify ...