Transfer learning enables solving a specific task having limited data by using the pre-trained deep networks trained on large-scale datasets. Typically, while transferring the learned knowledge from source task to the target task, the last few layers are fine-tuned (re-trained) over the target dataset. However, these layers are originally designed for the source task that might not be suitable for the target task. In this paper, we introduce a mechanism for automatically tuning the Convolutional Neural Networks (CNN) for improved transfer learning. The pre-trained CNN layers are tuned with the knowledge from target data using Bayesian Optimization. First, we train the final layer of the base CNN model by replacing the number of neurons in the softmax layer with the number of classes involved in the target task. Next, the CNN is tuned automatically by observing the classification performance on the validation data (greedy criteria). To evaluate the performance of the proposed method, experiments are conducted on three benchmark datasets, e.g., CalTech-101, CalTech-256, and Stanford Dogs. The classification results obtained through the proposed AutoTune method outperforms the standard baseline transfer learning methods over the three datasets by achieving 95.92%, 86.54%, and 84.67% accuracy over CalTech-101, CalTech-256, and Stanford Dogs, respectively. The experimental results obtained in this study depict that tuning of the pre-trained CNN layers with the knowledge from the target dataset confesses better transfer learning ability. The source codes are available at https://github.com/JekyllAndHyde8999/AutoTune_CNN_TransferLearning.

转移学习可以通过使用在大规模数据集上训练的预训练深度网络来解决数据有限的特定任务。通常，在将学习到的知识从源任务转移到目标任务的同时，最后几层将在目标数据集上进行微调（重新训练）。但是，这些层最初是为源任务设计的，可能不适合目标任务。在本文中，我们介绍了一种自动调整卷积神经网络（CNN）的机制，以改善转移学习。使用贝叶斯优化，利用来自目标数据的知识对预训练的CNN层进行调整。首先，我们用目标任务中涉及的类数替换softmax层中的神经元数，从而训练基本CNN模型的最后一层。下一个，通过观察验证数据（贪婪标准）的分类性能，可以自动调整CNN。为了评估所提出方法的性能，对三个基准数据集（例如CalTech-101，CalTech-256和Stanford Dogs）进行了实验。通过拟议的AutoTune方法获得的分类结果在三个数据集上均优于标准基线迁移学习方法，分别比CalTech-101，CalTech-256和Stanford Dogs达到95.92％，86.54％和84.67％的准确性。在这项研究中获得的实验结果表明，利用来自目标数据集的知识对经过预训练的CNN层进行调整，表明其具有更好的转移学习能力。可从https://github.com/JekyllAndHyde8999/AutoTune_CNN_TransferLearning获取源代码。