Deep Convolutional Neural Networks have pushed the state-of-the art for semantic segmentation provided that a large amount of images together with pixel-wise annotations is available. Data collection is expensive and a solution to alleviate it is to use transfer learning. This reduces the amount of annotated data required for the network training but it does not get rid of this heavy processing step. We propose a method of transfer learning without annotations on the target task for datasets with redundant content and distinct pixel distributions. Our method takes advantage of the approximate content alignment of the images between two datasets when the approximation error prevents the reuse of annotation from one dataset to another. Given the annotations for only one dataset, we train a first network in a supervised manner. This network autonomously learns to generate deep data representations relevant to the semantic segmentation. Then the images in the new dataset, we train a new network to generate a deep data representation that matches the one from the first network on the previous dataset. The training consists in a regression between feature maps and does not require any annotations on the new dataset. We show that this method reaches performances similar to a classic transfer learning on the PASCAL VOC dataset with synthetic transformations.
翻译:深革命神经网络已经推动了最先进的语义分解技术, 前提是可以提供大量图像和像素注释。 数据收集费用昂贵, 缓解它的方法是使用传输学习。 这减少了网络培训所需的附加说明数据数量, 但不会摆脱这一沉重的处理步骤。 我们提出了一个传输学习方法, 但不说明含有冗余内容和不同像素分布的数据集的目标任务。 我们的方法是利用两个数据集之间的图像的大致内容对齐, 当近似错误阻止从一个数据集再利用注释到另一个数据集时。 由于只对一个数据集进行说明, 我们以监督下的方式培训第一个网络。 这个网络自主地学会生成与语义分解相关的深度数据表。 在新数据集中的图像之后, 我们训练一个新的网络, 以生成一个与前一个数据集第一个网络相匹配的深层数据代表。 培训包含地貌地图之间的回归, 并且不需要在新数据集上作任何说明。 我们显示, 此方法与合成数据转换相似。 我们学习了常规数据转换方法。