Unsupervised Attribute Alignment for Characterizing Distribution Shift

Detecting and addressing distribution shift is an important task in machine learning. However, most of the machine learning solutions to deal with distribution shift lack the capability to identify the key characteristics of such a shift and present it to humans in an interpretable way. In this work, we propose a novel framework to compare two datasets and identify distribution shifts between the datasets. The key challenge is to identify generative factors of variation, which we refer to as attributes, that characterize the similarities and differences between the datasets. Producing this characterization requires finding a set of attributes that can be aligned between the two datasets and sets that are unique. We address this challenge through a novel approach that performs both attribute discovery and attribute alignment across the two distributions. We evaluate our algorithm’s effectiveness at accurately identifying these attributes in two separate experiments, one involving two variants of MNIST and a second experiment involving two versions of dSprites.