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"Data augmentation or how to fake your data" 🙂
Can you please share the code for color shifting?
I wish I had exact numbers. How much is too much Augmentation?
I have a question.. when we separate "conv"model and "softmax"model and save last conv layer output in disk and then use this as input of softmax model as last video(transfer learning), can't we use data augmentation? I've seen this information in "Deep learning with python" book but I can't understand why we can't use Data augmentation…
nice explantion
how are RGB values supposed to be negative, or is it just an 8-bit signed representation?
What are some thoughts on whether to apply augmentation on test dataset? There is an opinion to never augment test dataset because we want. test data to represent production. But isn't part of the reason we augment data in train because we don't have enough data and we augment it in ways we think reflect different real scenarios? If that is the case, then don't we want to apply augmentation also on test data?
hey. if i perform a k-fold cross validation on the augmented dataset, and i want to have balanced classes in both train and test sets (by default my dataset is imbalanced) is it a smart approach to augment seperately train and test set per each fold? so that the modified copies of original images do not land both in train and test set at the same time, and we avoid data leakage?