论文标题
组成卷积神经网络：一种具有天生鲁棒性的深层建筑
Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion
论文作者
论文摘要
最近的发现表明，深度卷积神经网络（DCNN）在部分阻塞下不能很好地概括。受构图模型在分类部分阻塞对象的成功的启发之中，我们建议将组成模型和DCNN集成到具有与部分闭塞的稳健性的统一深层模型中。我们称此体系结构组成卷积神经网络。特别是，我们建议将DCNN的完全连接的分类头替换为可区分的组成模型。组成模型的生成性质使其能够定位封闭器，然后专注于对象的非封闭部分。我们对MS-Coco数据集的部分遮挡对象进行了对人为的图像的分类实验，并进行了分类实验。结果表明，即使经过强烈用部分闭塞的数据进行训练，DCNN也不会牢固地对遮挡对象进行分类。我们提出的模型在对部分遮挡的对象进行分类时，通过很大的边距优于标准dcnn，即使在训练过程中未暴露于闭塞物体时，我们的模型也优于标准dcnn。其他实验表明，尽管仅接受了类标签训练，但组成词也可以准确地定位封闭器。这项工作中使用的代码公开可用。
Recent findings show that deep convolutional neural networks (DCNNs) do not generalize well under partial occlusion. Inspired by the success of compositional models at classifying partially occluded objects, we propose to integrate compositional models and DCNNs into a unified deep model with innate robustness to partial occlusion. We term this architecture Compositional Convolutional Neural Network. In particular, we propose to replace the fully connected classification head of a DCNN with a differentiable compositional model. The generative nature of the compositional model enables it to localize occluders and subsequently focus on the non-occluded parts of the object. We conduct classification experiments on artificially occluded images as well as real images of partially occluded objects from the MS-COCO dataset. The results show that DCNNs do not classify occluded objects robustly, even when trained with data that is strongly augmented with partial occlusions. Our proposed model outperforms standard DCNNs by a large margin at classifying partially occluded objects, even when it has not been exposed to occluded objects during training. Additional experiments demonstrate that CompositionalNets can also localize the occluders accurately, despite being trained with class labels only. The code used in this work is publicly available.
