Incremental Learning Framework Using Cloud Computing
This addresses a practical issue for data scientists and researchers using cloud computing or local machines, but it appears incremental as it builds on existing incremental learning concepts.
The paper tackles the problem of training neural networks when frequent disconnections or resource outages occur, by proposing a framework that enables progressive training without losing progress.
High volume of data, perceived as either challenge or opportunity. Deep learning architecture demands high volume of data to effectively back propagate and train the weights without bias. At the same time, large volume of data demands higher capacity of the machine where it could be executed seamlessly. Budding data scientist along with many research professionals face frequent disconnection issue with cloud computing framework (working without dedicated connection) due to free subscription to the platform. Similar issues also visible while working on local computer where computer may run out of resource or power sometimes and researcher has to start training the models all over again. In this paper, we intend to provide a way to resolve this issue and progressively training the neural network even after having frequent disconnection or resource outage without loosing much of the progress