MultiTune: Adaptive Integration of Multiple Fine-Tuning Models for Image Classification

Transfer learning has been widely used as a deep learning technique to solve computer vision related problems, especially when the problem is image classification employing Convolutional Neural Networks (CNN). In this paper, a novel transfer learning approach that can adaptively integrate multiple models with different fine-tuning settings is proposed, which is denoted as MultiTune. To evaluate the performance of MultiTune, we compare it to SpotTune, a state-of-the-art transfer learning technique. Two image datasets from the Visual Decathlon Challenge are used to evaluate the performance of MultiTune. The FGVC-Aircraft dataset is a fine-grained task and the CIFAR100 dataset is a more general task. Results obtained in this paper show that MultiTune outperforms SpotTune on both tasks. We also evaluate MultiTune on a range of target datasets with smaller numbers of images per class. MultiTune outperforms SpotTune on most of these smaller-sized datasets as well. MultiTune is also less computational than SpotTune and requires less time for training for each dataset used in this paper.