A Dictionary Approach to Domain-Invariant Learning in Deep Networks
A Dictionary Approach to Domain-Invariant Learning in Deep Networks
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发表时间：
2019-09
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通讯作者：
Ze Wang;Xiuyuan Cheng;G. Sapiro;Qiang Qiu
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作者：
Ze Wang;Xiuyuan Cheng;G. Sapiro;Qiang Qiu
在本文中，我们通过在卷积神经网络（CNN）中仅使用少量特定于域的参数来显式建模域偏移来考虑域不变深度学习。通过利用卷积滤波器可以很好地近似为一小组字典原子的线性组合的观察结果，我们首次从经验和理论上证明，通过将卷积层分解为特定于域的原子层和域共享系数层，可以有效地处理域移位，同时两者都保持卷积。输入通道现在将首先仅与每个相应的域特定字典原子进行空间卷积以“吸收”域变化，然后使用训练的公共分解系数线性组合输出通道以促进跨域的共享语义。我们使用玩具的例子，严格的分析，和现实世界的例子与不同的数据集和架构，显示建议的插件框架的有效性，在交叉和联合域的性能和域适应。使用所提出的架构，我们只需要一小组字典原子来建模每个额外的域，这带来了可以忽略不计的额外参数，通常是几百个。
In this paper, we consider domain-invariant deep learning by explicitly modeling domain shifts with only a small amount of domain-specific parameters in a Convolutional Neural Network (CNN). By exploiting the observation that a convolutional filter can be well approximated as a linear combination of a small set of dictionary atoms, we show for the first time, both empirically and theoretically, that domain shifts can be effectively handled by decomposing a convolutional layer into a domain-specific atom layer and a domain-shared coefficient layer, while both remain convolutional. An input channel will now first convolve spatially only with each respective domain-specific dictionary atom to "absorb" domain variations, and then output channels are linearly combined using common decomposition coefficients trained to promote shared semantics across domains. We use toy examples, rigorous analysis, and real-world examples with diverse datasets and architectures, to show the proposed plug-in framework's effectiveness in cross and joint domain performance and domain adaptation. With the proposed architecture, we need only a small set of dictionary atoms to model each additional domain, which brings a negligible amount of additional parameters, typically a few hundred.