Instance-based Deep Transfer Learning
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Deep transfer learning has acquired significant research interest. It makes use of pre-trained models that are learned from a source domain, and utilizes these models for the tasks in a target domain. Model-based deep transfer learning is arguably the most frequently used method. However, very little work has been devoted to enhancing deep transfer learning by focusing on the influence of data. In this work, we propose an instance-based approach to improve deep transfer learning in target domain. Specifically, we choose a pre-trained model which is learned from a source domain, and utilize this model to estimate the influence of each training sample in a target domain. Then we optimize training data of the target domain by removing the training samples that will lower the performance of the pre-trained model. We then fine-tune the pre-trained model with the optimized training data in the target domain, or build a new model which can be initialized partially based on the pre-trained model, and fine-tune it with the optimized training data in the target domain. Using this approach, transfer learning can help deep learning models to learn more useful features. Extensive experiments demonstrate the effectiveness of our approach on further boosting deep learning models for typical high-level computer vision tasks, such as image classification.
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