论文标题
Imagenet模型选择对域适应的影响
Impact of ImageNet Model Selection on Domain Adaptation
论文作者
论文摘要
深度神经网络被广泛用于图像分类问题。但是，很少的工作解决了不同深层神经网络的特征如何影响域的适应问题。现有方法通常从一个ImageNet模型中提取深度特征，而无需探索其他神经网络。在本文中，我们研究了不同的成像网模型如何影响域适应问题的转移精度。我们从16种不同的预训练的成像网模型中提取功能，并在使用这些功能时检查十二种基准方法的性能。广泛的实验结果表明，更高的精度成像网模型可产生更好的特征，并导致域适应问题的精度更高（相关系数高达0.95）。我们还检查了每个神经网络的架构，以找到用于特征提取的最佳层。总之，我们功能的性能超过了三个基准数据集中最先进的功能。
Deep neural networks are widely used in image classification problems. However, little work addresses how features from different deep neural networks affect the domain adaptation problem. Existing methods often extract deep features from one ImageNet model, without exploring other neural networks. In this paper, we investigate how different ImageNet models affect transfer accuracy on domain adaptation problems. We extract features from sixteen distinct pre-trained ImageNet models and examine the performance of twelve benchmarking methods when using the features. Extensive experimental results show that a higher accuracy ImageNet model produces better features, and leads to higher accuracy on domain adaptation problems (with a correlation coefficient of up to 0.95). We also examine the architecture of each neural network to find the best layer for feature extraction. Together, performance from our features exceeds that of the state-of-the-art in three benchmark datasets.
