Additional internet images give the neural network more examples of what cats do or do not look like. Because internet images and user-uploaded mobile-app images share some similarities, the neural network can apply some knowledge acquired from internet images to mobile-app images.

Shared Similarities Make Additional Training Data Useful

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

If a sufficiently large neural network is trained long enough on both mobile-app images and internet images, there is no theoretical harm in trying to make the algorithm do well on both sources.

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Merging user-uploaded images with internet images had a real risk of worsening performance for earlier learning algorithms, such as hand-designed computer-vision features followed by a simple linear classifier. With powerful, flexible algorithms such as large neural networks, that risk has greatly diminished.

Risk of Merging Training Data Sources Depends on Algorithm Flexibility

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