I cannot understand why distorting an image, e.g flipping it, increasing the gamma intensity would somehow increase the accuracy on neural network.
Within my situation, I am Using a CNN to detect if dogs are present in an image, and I was recommended to add distortion.
If we have the input of an image the CNN detects certain features and patterns that together make up a dog. If we only feed it images of a dog looking to the right it will not know that dogs can also look to the left, so it will not recognize the dog in a flipped image.
This increases the generalization potential of your network. The distortion technique does something similar but in a different way. An image is taken and slightly changed. For humans it is still clearly recognizable as a certain object, but the network gets a different input. This way it gets to learn slight variations of the same problems to see these are essentially the same. This once again increases the generalization potential.
