In a new release of Google titled “Revisiting Unreasonable Effectiveness of Data in Deep Learning Era,” Google describes what are the results of training a neural network on an image set of 300x times more images than in ImageNet.

The conclusion was that even with an increase of 3 million to 300 million training examples, the performance of the network linearly scales. Even after 300 million images, no flattening of the learning curve was observed.

To that end, the trained network placed a record in the COCO object detection benchmark. They came to the result that only the number of training data was increased, there were no improvements to the model itself.

This is an impressive demonstration of the importance of BigData in the context of deep learning. The best models can only be developed by companies that have the expertise to store and efficiently process enormous amounts of data.

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