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This is a terminology question: in the context of artificial neural networks, does multi-task learning occur iff the network has more than 1 output?

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Having multiple outputs is necessary but not sufficient for multi-task learning.

One task could be a multi-label classification task (e.g. is the input a picture of a human, a cat or a dog?) with one output node per target class. So a network with three outputs is performing a single task.

A second related task could be to predict what time of day the input photo was taken (e.g. morning, afternoon or night). So a multi-task network here could have six outputs defining the two tasks.

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  • $\begingroup$ Thanks. In the case of the multi classification human, a cat or a dog, couldn't one argue that therr are three different tasks: task 1 is it a picture of a human? Task 2 is it a picture of a cat? Etc $\endgroup$ – Franck Dernoncourt Jul 13 '15 at 16:03

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