A Post-Training Framework for Improving the Performance of Deep Learning Models via Model Transformation

Abstract

Deep learning (DL) techniques have attracted much attention in recent years and have been applied to many application scenarios. To improve the performance of DL models regarding different properties, many approaches have been proposed in the last decades, such as improving the robustness and fairness of DL models to meet the requirements for practical use. Among existing approaches, post-training is an effective method that has been widely adopted in practice due to its high efficiency and good performance. Nevertheless, its performance is still limited due to the incompleteness of training data. Additionally, existing approaches are always specifically designed for certain tasks, such as improving model robustness, which cannot be used for other purposes. In this paper, we aim to fill this gap and propose an effective and general post-training framework, which can be adapted to improve the model performance from different aspects. Specifically, it incorporates a novel model transformation technique that transforms a classification model into an isomorphic regression model for fine-tuning, which can effectively overcome the problem of incomplete training data by forcing the model to strengthen the memory of crucial input features and thus improve the model performance eventually. To evaluate the performance of our framework, we have adapted it to two emerging tasks for improving DL models, i.e., robustness and fairness improvement, and conducted extensive studies by comparing it with state-of-the-art approaches. The experimental results demonstrate that our framework is indeed general as it is effective in both tasks. Specifically, in the task of robustness improvement, our approach Dare has achieved the best results on 61.1% cases ( vs. 11.1% cases achieved by baselines). In the task of fairness improvement, our approach FMT can effectively improve the fairness without sacrificing the accuracy of the models.