When additional data is much more numerous than target-distribution data, limited computational resources can make it expensive to model both sources well. Down-weighting the auxiliary source can reduce the need for a very large neural network while still using the additional data.

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Adding internet images can force the neural network to expend some capacity learning properties specific to internet images, such as higher resolution or different framing. If those properties differ greatly from mobile-app images, less capacity remains for recognizing mobile-app data, and this could hurt performance.

Source-Specific Properties Can Consume Representation Capacity

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

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