Can I do oversampling by copying the same image multiple times? Will it effect my neural network accuracy? I am working on an image data-set. As you may have guessed it is imbalanced data. I have 'Class A, 19,000 images' and 'Class B, 2,876 images'.

So I did an undersampling by removing randomly from the majority class till it becomes equal to the minority class.

On doing this I am loosing lot of information from those 19000 images which I could get. 
So I do an oversampling of minority class, by simply copying the 2,876 images again and again.

Is this undersampling method correct, will it effect my accuracy? I trained an Inceptionv4 model using this oversampled data and it is not at all stable and I am getting poor accuracy.

What should be my strategy ?
This would be a pretty fun opportunity to experiment a bit. I know there's been breakthroughs in OCR technology by training the network on images that have been altered.

Long story short, there was a pre-process that took the source images and did minor manipulations to them. This involved skewing, rotating, changing resolution &amp; adding artifacts into the image to introduce "Noise". This allowed the network to learn to recognize characters more accurately. It may be worthwhile to create an image pre-processing pipeline that will do a variation of modifications to the source images. When training you may want to lower your learning rate &amp; split up your data into 70% train &amp; 30% test. Then you can loop through all the images repeatedly until you hit a maximum for the learning rate.

Some examples
https://matthewearl.github.io/2016/05/06/cnn-anpr/
https://medium.com/@shreyas.s/image-data-generation-for-optical-character-recognition-ocr-9b19300649c8