Core Concepts
SortedNet proposes a scalable solution for training many-in-one neural networks by leveraging sorted architectures and stochastic training.
Abstract
Abstract
Deep neural networks (DNNs) face challenges in catering to diverse user needs efficiently.
SortedNet proposes a generalized solution to train numerous sub-models simultaneously.
Introduction
Users demand models that can adapt to dynamic conditions, posing a challenge for conventional neural networks.
SortedNet aims to harness the modularity of DNNs to enhance performance and practical deployment.
Proposed Method
SortedNet sorts sub-models based on computation/accuracy and trains them using a novel updating scheme.
The method enables efficient switching between sub-models during inference.
Experiments
SortedNet outperforms existing dynamic training methods across various architectures and tasks.
The method is scalable, generalizable, and offers speed-up in inference time.
Conclusion
SortedNet offers a promising approach for training dynamic neural networks efficiently.
Stats
SortedNet는 여러 하위 모델을 동시에 훈련하여 최대 96%의 성능을 달성할 수 있습니다.
SortedNet은 한 번에 최대 160개의 하위 모델을 훈련할 수 있습니다.
Quotes
"For every minute spent organizing, an hour is earned." - Benjamin Franklin.