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There is an increasing number of pre-trained deep neural network models.However, it is still unclear how to effectively use these models for a newtask. Transfer learning, which aims to transfer knowledge from source tasks toa target task, is an effective solution to this problem. Fine-tuning is apopular transfer learning technique for deep neural networks where a few roundsof training are applied to the parameters of a pre-trained model to adapt themto a new task. Despite its popularity, in this paper, we show that fine-tuningsuffers from several drawbacks. We propose an adaptive fine-tuning approach,called AdaFilter, which selects only a part of the convolutional filters in thepre-trained model to optimize on a per-example basis. We use a recurrent gatednetwork to selectively fine-tune convolutional filters based on the activationsof the previous layer. We experiment with 7 public image classificationdatasets and the results show that AdaFilter can reduce the averageclassification error of the standard fine-tuning by 2.54%.

AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning