What are some challenges in training CNNs?

Current approaches to object recognition make essential use of machine learning methods. To im- prove their performance, we can collect larger datasets, learn ... 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. Feb 1, 2023 ... A new bio-inspired deep learning model that enables generalization in dynamic lighting conditions. 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 ... Augmented graph representations are further input to a custom CNN deep learning architecture with a split of 70% train, 10% validation and 20% test ... Fundamental difference between convolutional neural network (CNN) and conventional machine learning is that, rather than using hand-crafted features, such as ... 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. Aug 26, 2020 ... Object detection: With CNN, we now have sophisticated models like R-CNN ... machine learning, and artificial intelligence professionals. Sep 2, 2023 ... Image processing using Deep learning models CNN · Want to upload an image dataset? · Use of Deep Learning specifically CNN which is normally ... We believe this suggests significant avenues of future exploration for machine learning language design, pedagogy, and implementation, both inside and outside ...