Back pressure is often mentioned in the context of data streams (TCP for example.) Back pressure refers to a mechanism where a data receiving component is signalling that the incoming data stream should slow down, or speed up. Apache Flink for example handles back pressure well. But that is not what this post is about. It is about adding extra loss functions to neural nets, and how that can improve overall performance.
Consider the picture at the end of the post. This picture is part of a Kaggle competition. (Note: I heavily cropped and blurred the image to FUBAR it and avoid any rights or recognition issues.) The goal of the competition is to classify the fish that is being caught using machine learning.
Here is the remarkable thing. One could straight-forward classify the picture as is using deep-learning. The net would figure out that the fish is the relevant part, and it would then figure out what type of fish it is, ie. classify it. Alternatively one could first extract the fish from the picture, and then classify the extracted hopefully fishy image segment using a separate neural net.
Here it comes. Adding extra bounding box parameters to the initial fish classification net actually improves the classification rate. Yes, a net that only classifies fish, classifies less accurate than a net that classifies a fish and learns the bounding box.
By providing richer feedback about the subject, the internal structure of the net seems to become better. For lack of a better word, I call it back-pressure. Counter intuitive. More is less; error that is.