Deep neural networks with transfer learning in millet crop images

– Convolution layers & Pooling layers + global architecture – Training algorithm + Dropout Regularization.. • Useful

In the first part, a series of experiments were carried out to use the Vgg transfer learning features to evaluate the performance of the proposed unimodal biometric system using

 pushing forward to weakly supervised learning the problem Medical Image Segmentation of Thoracic Organs at Risk in CT-images..

In this paper, we propose natural attacks using visibly foggy images to generate input that causes incorrect classi- fication by the Inception deep learning model [22].. Apart from

The proposed algorithms for processing images based on the extraction and compression of features using neural networks for colorization of black and white images are based

A neural network algorithm for labeling images in text documents is developed on the basis of image preprocessing that simplifies the localization of individual parts of a

As deep learning models show an impressive ability to recognize patterns in images, many researchers propose deep learn- ing based steganalysis methods recently.. [11] reported

As seen in figure 6, we reduced the training time, and the difference of time reduced became higher when we increased the number of iteration for each ap- proach, in which we