In this paper, we study a new representation-learning task, which we termed as disassembling object representations. Given an image featuring multiple objects, the goal of disassembling is to acquire a latent representation, of which each part corresponds to one category of objects. Disassembling thus finds its application in a wide domain such as image editing and few- or zero-shot learning, as it enables category-specific modularity in the learned representations. To this end, we propose an unsupervised approach to achieving disassembling, named Unsupervised Disassembling Object Representation (UDOR). UDOR follows a double auto-encoder architecture, in which a fuzzy classification and an object-removing operation are imposed. The fuzzy classification constrains each part of the latent representation to encode features of up to one object category, while the object-removing, combined with a generative adversarial network, enforces the modularity of the representations and integrity of the reconstructed image. Furthermore, we devise two metrics to respectively measure the modularity of disassembled representations and the visual integrity of reconstructed images. Experimental results demonstrate that the proposed UDOR, despite unsupervised, achieves truly encouraging results on par with those of supervised methods.

在本文中，我们研究了一种新的表征学习任务，我们称之为分解对象表征。给定具有多个对象的图像，分解的目标是获取潜在表示，其中每个部分对应于一类对象。因此，反汇编在广泛的领域中得到了应用，例如图像编辑和少样本或零样本学习，因为它可以在学习的表示中实现特定于类别的模块化。为此，我们提出了一种实现反汇编的无监督方法，称为无监督反汇编对象表示（UDOR）。UDOR 遵循双自动编码器架构，其中强加了模糊分类和对象移除操作。模糊分类将潜在表示的每一部分限制为编码最多一个对象类别的特征，而对象去除与生成对抗网络相结合，加强了表示的模块化和重建图像的完整性。此外，我们设计了两个指标来分别测量分解表示的模块化和重建图像的视觉完整性。实验结果表明，尽管没有监督，所提出的 UDOR 取得了与监督方法相当的令人鼓舞的结果。我们设计了两个指标来分别测量分解表示的模块化和重建图像的视觉完整性。实验结果表明，尽管没有监督，所提出的 UDOR 取得了与监督方法相当的令人鼓舞的结果。我们设计了两个指标来分别测量分解表示的模块化和重建图像的视觉完整性。实验结果表明，尽管没有监督，所提出的 UDOR 取得了与监督方法相当的令人鼓舞的结果。