Tree DNN: A Deep Container Network
Brijraj Singh, Swati Gupta, Mayukh Das, Praveen Doreswamy Naidu,, Sharan Kumar Allur

TL;DR
TreeDNN is a novel deep network architecture designed for multi-task learning across different datasets, enabling efficient training, reduced storage, and faster inference by selectively loading branches.
Contribution
The paper introduces TreeDNN, a new architecture that allows multi-dataset training with branch-specific data, improving efficiency and responsiveness over traditional methods.
Findings
Competitive performance with reduced parameter storage
Faster inference by loading specific branches
Effective multi-dataset training with TreeDNN
Abstract
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Neural Network Applications · Neural Networks and Applications · Machine Learning and Data Classification
