I would like to add some more samples to my dataset which consists of two parts: 1. image and 2. numerical data. For each image in the dataset there is its corresponding numerical data as well. If it were to image only classification, I would have used cropping augmentation method (others wouldn't not suit the dataset). However, it is now a multiple input to one output (binary classification) task, I'm struggling to come up with a better solution.
Could someone name some of the techniques I can apply to such a type of classification?
One example I can think of is this. Since I can't apply other augmentation methods except cropping, I may increase one sample of data by cropping the center, left and right parts (by preserving ~90% of original image).
If this type of image augmentation is still sensible, I would just broadcast the same number of numerical samples (namely, 3 times) as one image's numerical data.
What do you think of this way of data augmentation?