The advantage of adversarial domain adaptation is that it uses the idea of adversarial adaptation to confuse the feature distribution of two domains and solve the problem of domain transfer in transfer learning. However, although the discriminator completely confuses the two domains, adversarial domain adaptation still cannot guarantee the consistent feature distribution of the two domains, which may further deteriorate the recognition accuracy. Therefore, in this article, we propose a deep adversarial domain adaptation network, which optimises the feature distribution of the two confused domains by adding multi-kernel maximum mean discrepancy to the feature layer and designing a new loss function to ensure good recognition accuracy. In the last part, some simulation results based on the Office-31 and Underwater data sets show that the deep adversarial domain adaptation network can optimise the feature distribution and promote positive transfer, thus improving the classification accuracy.

中文翻译：

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深度对抗域适应网络

对抗域适应的优势在于它利用对抗性适应的思想混淆了两个域的特征分布，解决了迁移学习中的域迁移问题。然而，虽然鉴别器完全混淆了两个域，但对抗域自适应仍然不能保证两个域的特征分布一致，这可能进一步恶化识别精度。因此，在本文中，我们提出了一种深度对抗域自适应网络，通过在特征层添加多核最大均值差异并设计新的损失函数来优化两个混淆域的特征分布，以确保良好的识别精度。在最后一部分，