论文标题
自我半监督的神经体系结构搜索语义细分
Self Semi Supervised Neural Architecture Search for Semantic Segmentation
论文作者
论文摘要
在本文中，我们提出了一种基于自我监督和半监督学习的神经体系结构搜索策略，以实现语义细分任务。我们的方法通过共同解决了通过无标记的培训数据而通过自我监督的学习发现的拼图借口任务，为该任务构建了优化的神经网络（NN）模型，并通过半手不足的学习来利用未标记的数据的结构。 NN模型的体系结构的搜索是通过使用梯度下降算法动态路由来执行的。 CityScapes和Pascal VOC 2012数据集的实验表明，发现的神经网络比最先进的手工制作的NN模型更有效，其浮动操作少四倍。
In this paper, we propose a Neural Architecture Search strategy based on self supervision and semi-supervised learning for the task of semantic segmentation. Our approach builds an optimized neural network (NN) model for this task by jointly solving a jigsaw pretext task discovered with self-supervised learning over unlabeled training data, and, exploiting the structure of the unlabeled data with semi-supervised learning. The search of the architecture of the NN model is performed by dynamic routing using a gradient descent algorithm. Experiments on the Cityscapes and PASCAL VOC 2012 datasets demonstrate that the discovered neural network is more efficient than a state-of-the-art hand-crafted NN model with four times less floating operations.
