The dominant line of work in domain adaptation has focused on learning
invariant representations using domain-adversarial training. In this paper, we
interpret this approach from a game theoretical perspective. Defining optimal
solutions in domain-adversarial training as a local Nash equilibrium, we show
that gradient descent in domain-adversarial training can violate the asymptotic
convergence guarantees of the optimizer, oftentimes hindering the transfer
performance. Our analysis leads us to replace gradient descent with high-order
ODE solvers (i.e., Runge-Kutta), for which we derive asymptotic convergence
guarantees. This family of optimizers is significantly more stable and allows
more aggressive learning rates, leading to high performance gains when used as
a drop-in replacement over standard optimizers. Our experiments show that in
conjunction with state-of-the-art domain-adversarial methods, we achieve up to
3.5% improvement with less than of half training iterations. Our optimizers are
easy to implement, free of additional parameters, and can be plugged into any
domain-adversarial framework.

本文从博弈论的角度解释了域自适应训练中学习不变表示的支配性思路，并将梯度下降的优化器替换成高阶 ODE 求解器，为此得出渐近收敛保证。实验结果表明，与标准优化器相比，使用我们的优化器能够在半数训练迭代次数内，与最先进的域自适应方法相结合实现 3.5％的性能提升。

面向领域的对抗性训练：博弈视角

Domain Adversarial Training: A Game Perspective

We present a Lipreading system, i.e. a speech recognition system using only
visual features, which uses domain-adversarial training for speaker
independence. Domain-adversarial training is integrated into the optimization
of a lipreader based on a stack of feedforward and LSTM (Long Short-Term
Memory) recurrent neural networks, yielding an end-to-end trainable system
which only requires a very small number of frames of untranscribed target data
to substantially improve the recognition accuracy on the target speaker. On
pairs of different source and target speakers, we achieve a relative accuracy
improvement of around 40% with only 15 to 20 seconds of untranscribed target
speech data. On multi-speaker training setups, the accuracy improvements are
smaller but still substantial.

本文介绍了一种唇读系统，即一种只使用视觉特征的语音识别系统，它利用了领域对抗训练等技术以实现说话人无关性，用于优化由一系列前馈神经网络和 LSTM 递归神经网络构成的唇读者，并实现了端到端可训练的系统，只需要少量带有未转录目标数据的 frames 即可在目标说话人的语音识别准确率上显著提高约 40％。