How can we ensure that the development and use of CNNs align with human values and societal goals?

... intelligence must solve. One of the solutions is to apply deep learning and computer vision. Convolutional Neural Networks' (CNN) deep architecture ... Jun 28, 2021 ... Fast R-CNN brings feature extraction and region selection into a single machine learning model. Fast R-CNN receives an image and a set of ... This paper presents “EspiNet V2” a Deep Learning model, based on the region-based detector Faster R-CNN. The model is used for the detection of motorcycles ... Mar 9, 2024 ... A 1-dimensional convolutional neural network (1D CNN) model is developed to process deep learning of raw impedance signals for smart ... 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. Jul 19, 2021 ... To learn how to train your first CNN with PyTorch, just keep reading. CNNs are a type of deep learning algorithm that can analyze and extract ... Oct 2, 2023 ... ... machine learning techniques to overcome the non ... CNN's learned associations related to ejection events in wall-bounded turbulent flows. Feb 1, 2023 ... Convolutional Neural Networks (CNNs) is a powerful deep learning architecture well-suited to image classification and object recognition tasks. The Machine Learning team at CNN Digital is dedicated to research, build and evaluate Machine Learning and AI capabilities at CNN. The team is comprised of ... Dec 7, 2017 ... While people usually tend to simply resize any image into a square while training a CNN (for example, resnet takes a 224x224 square image), that looks ugly to ...