Deep learning models have a large number of freeparameters that need to be calculated by effective trainingof the models on a great deal of training data to improvetheir generalization performance. However, data obtaining andlabeling is expensive in practice. Data augmentation is one of themethods to alleviate this problem. In this paper, we conduct apreliminary study on how three variables (augmentation method,augmentation rate and size of basic dataset per label) can affectthe accuracy of deep learning for image classification. The studyprovides some guidelines: (1) it is better to use transformationsthat alter the geometry of the images rather than those justlighting and color. (2) 2-3 times augmentation rate is good enoughfor training. (3) the smaller amount of data, the more obviouscontributions could have.
Affine image transformations are performed in an interleaved manner, whereby coordinate transformations and intensity calculations are alternately performed incrementally on small portions of an image. The pixels are processed in rows such that after coordinates of a first pixel are determined for reference, each pixel in a row, and then pixels in vertically adjacent rows, are processed relative to the coordinates of the previously processed adjacent pixels. After coordinate transformation to produce affine translation, rotation, skew, and or scaling, intermediate metapixels are vertically split and shifted to eliminate holes and overlaps. Intensity values of output metapixels are calculated as being proportional to the sum of scaled portions of the intermediate metapixels which cover the output pixels respectively.