论文标题
分支专业化及其在图像分解中的应用
Analysis of Branch Specialization and its Application in Image Decomposition
论文作者
论文摘要
分支神经网络已广泛用于各种任务。分支是执行独立处理的模型的子部分，然后进行聚合。众所周知，这种设置引起了一种称为分支专业化的现象，其中不同的分支成为不同子任务的专家。这种观察本质上是定性的。在这项工作中，我们提出了分支专业化的方法论分析。我们解释了梯度下降在这种现象中的作用。我们表明，分支的生成网络自然会将动物图像分解为有意义的毛皮，晶须和斑点以及面对图像的频道，并将图像转变为诸如不同的照明组件和面部零件等频道。
Branched neural networks have been used extensively for a variety of tasks. Branches are sub-parts of the model that perform independent processing followed by aggregation. It is known that this setting induces a phenomenon called Branch Specialization, where different branches become experts in different sub-tasks. Such observations were qualitative by nature. In this work, we present a methodological analysis of Branch Specialization. We explain the role of gradient descent in this phenomenon. We show that branched generative networks naturally decompose animal images to meaningful channels of fur, whiskers and spots and face images to channels such as different illumination components and face parts.
