The recent advances in deep transfer learning reveal that adversarial
learning can be embedded into deep networks to learn more transferable features
to reduce the distribution discrepancy between two domains. Existing
adversarial domain adaptation methods either learn a single domain
discriminator to align the global source and target distributions or pay
attention to align subdomains based on multiple discriminators. However, in
real applications, the marginal (global) and conditional (local) distributions
between domains are often contributing differently to the adaptation. There is
currently no method to dynamically and quantitatively evaluate the relative
importance of these two distributions for adversarial learning. In this paper,
we propose a novel Dynamic Adversarial Adaptation Network (DAAN) to dynamically
learn domain-invariant representations while quantitatively evaluate the
relative importance of global and local domain distributions. To the best of
our knowledge, DAAN is the first attempt to perform dynamic adversarial
distribution adaptation for deep adversarial learning. DAAN is extremely easy
to implement and train in real applications. We theoretically analyze the
effectiveness of DAAN, and it can also be explained in an attention strategy.
Extensive experiments demonstrate that DAAN achieves better classification
accuracy compared to state-of-the-art deep and adversarial methods. Results
also imply the necessity and effectiveness of the dynamic distribution
adaptation in adversarial transfer learning.

该论文提出了一种新的动态对抗自适应网络（DAAN），旨在在量化评估全局和本地域分布的相对重要性的同时，动态学习域不变表示。DAAN 是首次尝试对深层对抗学习进行动态自适应分布的实践。该方法在真实应用中易于实现和训练，并取得了比现有方法更好的分类准确性，结果表明对抗迁移学习中动态分布自适应的必要性和有效性。

动态对抗适应网络的迁移学习

Transfer Learning with Dynamic Adversarial Adaptation Network

Transfer learning has achieved promising results by leveraging knowledge from
the source domain to annotate the target domain which has few or none labels.
Existing methods often seek to minimize the distribution divergence between
domains, such as the marginal distribution, the conditional distribution or
both. However, these two distances are often treated equally in existing
algorithms, which will result in poor performance in real applications.
Moreover, existing methods usually assume that the dataset is balanced, which
also limits their performances on imbalanced tasks that are quite common in
real problems. To tackle the distribution adaptation problem, in this paper, we
propose a novel transfer learning approach, named as Balanced Distribution
\underline{A}daptation~(BDA), which can adaptively leverage the importance of
the marginal and conditional distribution discrepancies, and several existing
methods can be treated as special cases of BDA. Based on BDA, we also propose a
novel Weighted Balanced Distribution Adaptation~(W-BDA) algorithm to tackle the
class imbalance issue in transfer learning. W-BDA not only considers the
distribution adaptation between domains but also adaptively changes the weight
of each class. To evaluate the proposed methods, we conduct extensive
experiments on several transfer learning tasks, which demonstrate the
effectiveness of our proposed algorithms over several state-of-the-art methods.

本文提出了一种名为平衡分布适应（BDA）的新型迁移学习方法，该方法可以自适应地利用边际和条件分布差异的重要性，并提出了一种新的加权平衡分布适应（W-BDA）算法来解决转移学习中的类别不平衡问题。在多个迁移学习任务中进行的广泛实验表明，与几种先进方法相比，所提出的算法的有效性。