Auxiliary Learning as a step towards Artificial General Intelligence
This addresses the issue of handling unknown categories in machine learning models, which is incremental as it builds on existing classification frameworks.
The paper tackles the problem of increasing the generality of narrow neural networks by handling unknown objects through Auxiliary Learning, using a Cat & Dog binary classifier as an example, but does not provide concrete numerical results.
Auxiliary Learning is a machine learning approach in which the model acknowledges the existence of objects that do not come under any of its learned categories.The name Auxiliary learning was chosen due to the introduction of an auxiliary class. The paper focuses on increasing the generality of existing narrow purpose neural networks and also highlights the need to handle unknown objects. The Cat & Dog binary classifier is taken as an example throughout the paper.