  Multi-Task Learning (MTL) has shown its importance at user products for fast
training, data efficiency, reduced overfitting etc. MTL achieves it by sharing
the network parameters and training a network for multiple tasks
simultaneously. However, MTL does not provide the solution, if each task needs
training from a different dataset. In order to solve the stated problem, we
have proposed an architecture named TreeDNN along with it's training
methodology. TreeDNN helps in training the model with multiple datasets
simultaneously, where each branch of the tree may need a different training
dataset. We have shown in the results that TreeDNN provides competitive
performance with the advantage of reduced ROM requirement for parameter storage
and increased responsiveness of the system by loading only specific branch at
inference time.
