Neural Network Retraining for Model Serving

TL;DR

This paper introduces a novel incremental retraining approach for neural networks in model serving, addressing catastrophic forgetting and efficiency through sample and weight selection with bandits and a new regularization.

Contribution

It proposes a new retraining model that balances data and weight importance using multi-armed bandits and a regularization term to prevent forgetting during life-long learning.

Findings

Mitigates catastrophic forgetting effectively.

Improves model performance with incremental retraining.

Reduces computational resources needed for retraining.

Abstract

We propose incremental (re)training of a neural network model to cope with a continuous flow of new data in inference during model serving. As such, this is a life-long learning process. We address two challenges of life-long retraining: catastrophic forgetting and efficient retraining. If we combine all past and new data it can easily become intractable to retrain the neural network model. On the other hand, if the model is retrained using only new data, it can easily suffer catastrophic forgetting and thus it is paramount to strike the right balance. Moreover, if we retrain all weights of the model every time new data is collected, retraining tends to require too many computing resources. To solve these two issues, we propose a novel retraining model that can select important samples and important weights utilizing multi-armed bandits. To further address forgetting, we propose a new regularization term focusing on synapse and neuron importance. We analyze multiple datasets to document the outcome of the proposed retraining methods. Various experiments demonstrate that our retraining methodologies mitigate the catastrophic forgetting problem while boosting model performance.