Image Augmentation using Radial Transform for Training Deep Neural Networks

Deep learning models have a large number of free parameters that must be estimated by efficient training of the models on a large number of training data samples to increase their generalization performance. In real-world applications, the data available to train these networks is often limited or imbalanced. We propose a sampling method based on the radial transform in a polar coordinate system for image augmentation to facilitate the training of deep learning models from limited source data. This pixel-wise transform provides representations of the original image in the polar coordinate system by generating a new image from each pixel. This technique can generate radial transformed images up to the number of pixels in the original image to increase the diversity of poorly represented image classes. Our experiments show improved generalization performance in training deep convolutional neural networks with radial transformed images.Comment: This paper is accepted for presentation at IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE ICASSP), 201

Status reports to the Papermaking Project Advisory Committee, March 23, 1998

"March 23, 1998."Fundamentals of drying: status report for project F001 / David I. Orloff ... [et al.] ; Delamination buckling and spalling of plasma sprayed thermal barrier coating for impulse drying rolls / Tim Patterson and David I. Orloff ; Fundamentals of web heating: status report for project F002 / Timothy F. Patterson ... [et al.] ; Fundamentals of coating systems: status report for project F003 / Cyrus K. Aidun ; Approach flow systems: status report for project F004 / Xiaodong Wang, Frederick Bloom, Zhigang Feng ; Flow induced oscillations of submerged and inclined concentric pipes with different lengths / Xiaodong Wang and Frederick Bloom ; Single jet mixing at arbitrary angle in turbulent tube flow / Zhigang Feng, Xiaodong Wang, Larry J. Forney ; Fundamentals of headbox and forming hydrodynamics: status report for project F005 / Cyrus K. Aidun, Paul McKay, Xiao-Liang Ye -- Slide Material