A compositional parts based model for object recognition

基于卷积神经网络（cnn）的深度学习架构是物体识别和分割的最新技术。然而，cnn需要数以百万计的标记图像才能达到这种性能。它们还缺乏可解释性，这使得很难理解为什么有些图像被错误分类。最近对这类系统的对抗性攻击的研究突出了这个问题。在网络架构的进步和大规模GPU训练使cnn达到目前的性能之前，cnn的替代品，如基于部件的组合模型，在过去可以与cnn的性能相媲美或超过cnn的性能。基于部件的组合模型可以克服cnn的一些缺点，但在大规模数据集上存在可扩展性差的问题。使用神经网络组件构建的组合模型，但形成一个可解释的组合图，原则上将两种方法的优点结合在一个可扩展的、可解释的模型中。

Deep Learning architectures based on Convolutional Neural Networks (CNNs) are the state of the art technique for object recognition and segmentation. However, CNNs require millions of labelled images to reach this performance. They also suffer from a lack of interpretability making it difficult to understand why some images are misclassified. Recent work on adversarial attacks on these types of systems has highlighted this problem. Alternatives to CNNs such as parts based compositional models rivalled or surpassed CNNs performance in the past before the advances in network architecture and large-scale GPU training allowed CNNs to reach their current performance. Parts based compositional models can overcome some of the weaknesses of CNNs, but themselves suffer from poor scalability on large-scale datasets. A compositional model built using neural network components, but forming an interpretable compositional graph would in principle combine the advantages of both approaches in a scaleable, interpretable model.