论文标题
如何在几次学习中微调深度神经网络？
How to fine-tune deep neural networks in few-shot learning?
论文作者
论文摘要
深度学习已被广泛用于数据密集型应用程序中。但是，培训深度神经网络通常需要大量数据集。如果没有足够的数据进行培训，那么深度学习模型的性能甚至比浅网络的更糟。已经证明，很少有训练样本可以概括为新任务。深度模型的微调是简单有效的几次学习方法。但是，如何微调深度学习模型（微调卷积层或BN层？）仍然缺乏深入研究。因此，我们研究了如何通过本文实验比较来微调深层模型。此外，分析模型的重量以验证微调方法的可行性。
Deep learning has been widely used in data-intensive applications. However, training a deep neural network often requires a large data set. When there is not enough data available for training, the performance of deep learning models is even worse than that of shallow networks. It has been proved that few-shot learning can generalize to new tasks with few training samples. Fine-tuning of a deep model is simple and effective few-shot learning method. However, how to fine-tune deep learning models (fine-tune convolution layer or BN layer?) still lack deep investigation. Hence, we study how to fine-tune deep models through experimental comparison in this paper. Furthermore, the weight of the models is analyzed to verify the feasibility of the fine-tuning method.
