Adapting transfer learning models to dataset through pruning and Avg-TopK pooling

Abstract

AbstractThis study focuses on efficiently adapting transfer learning models to address the challenges of creating customized deep learning models for specific datasets. Designing a model from scratch can be time-consuming and complex due to factors like model complexity, size, and dataset structure. To overcome these obstacles, a novel approach is proposed using transfer learning models. The proposed method involves identifying relevant layers in transfer learning models and removing unnecessary ones using a layer-based variance pruning technique. This results in the creation of new models with improved computational efficiency and classification performance. By streamlining the models through layer-based variance pruning, the study achieves enhanced accuracy and faster computation. Experiments were conducted using the COVID-19 dataset and well-known transfer learning models, including InceptionV3, ResNet50V2, DenseNet201, VGG16, and Xception to validate the approach. Among these models, the variance-based layer pruning technique was applied to InceptionV3 and DenseNet201, yielding the best results. When these pruned models were combined with the new pooling layer, Avg-TopK, the proposed method achieved an outstanding image classification accuracy of 99.3%. Comparisons with previous models and literature studies indicate that the proposed approach outperforms existing methods, showcasing state-of-the-art performance. This high-performance approach provides great potential for diagnosing COVID-19 and monitoring disease progression, especially on hardware-limited devices. By leveraging transfer learning models, pruning, and efficient pooling techniques, the study presents a promising strategy for tackling challenges in custom model design, leading to exceptional results in such as image classification and segmentation tasks. The proposed methodology holds the potential to yield exceptional outcomes across a spectrum of tasks, encompassing disciplines such as image classification and segmentation.