University of Michigan - Ann Arbor

- Start off trying to have one neural network do multiple things (can actually lead to better performance)
- differences and similarities amongst tasks is utilized to output a better result

Multi-task Learning

- When you are training on a set of tasks that could benefit from having shared lower-level features
- Usually: amount of data you have for each task is quite similar
- Can train big enough neural networks that are able  to do well on all tasks


When to use multi-task learning?

1) Hard parameter sharing
2) Soft parameter sharing


Two types of Multi task learning methods


- Samples sizes are increased as a general relationship to be found amongst all parameters is typically the goal
- Multi task learning helps focus data attention to relevant noise, ignoring less relevant data
- Multi task learning is a regularizer meaning it reduces overfitting


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