Title: MAGA: Modeling a Group Action. Abstract: Combinatorial generalization, an ability to collect various attributes from diverse data and assemble them to generate novel unexperienced data, is considered an essential traversal point to achieve human-level intelligence.
Previous unsupervised approaches mainly focused on  learning the disentangled representation, such as the variational autoencoder. However, recent studies discovered that the disentangled representation is insufficient for combinatorial generalization and is not even correlated. In this regard, we proposed a novel framework of data generation that can robustly generalize under these distribution shift situations. The model, simulating the group action, carries out combinatorial generalization by discovering the fundamental transformation between the data. We conducted experiments on the two settings: Recombination-to-Element, and Recombination-to-Range. The experiments demonstrated that our method has quantitatively and qualitatively superior generalizability and generates better images over traditional models.
