Abstract. We study the problem of building models that can transfer selected attributes from one image to another without affecting the other attributes. Towards this goal, we develop analysis and a training methodology for autoencoding models, whose encoded features aim to disentangle attributes. These features are explicitly split into two components: one that should represent attributes in common between pairs of images, and another that should represent attributes that change between pairs of images. We show that achieving this objective faces two main challenges: One is that the model may learn degenerate mappings, which we call shortcut problem, and the other is that the attribute representation for an image is not guaranteed to follow the same interpretation on another image, which we call reference ambiguity. To address the shortcut problem, we introduce novel constraints on image pairs and triplets and show their effectiveness both analytically and experimentally. In the case of the reference ambiguity, we formally prove that a model that guarantees an ideal feature separation cannot be built. We validate our findings on several datasets and show that, surprisingly, trained neural networks often do not exhibit the reference ambiguity