  Multi-attribute classification generalizes classification, presenting new
challenges for making accurate predictions and quantifying uncertainty. We
build upon recent work and show that architectures for multi-attribute
prediction can be reinterpreted as energy-based models (EBMs). While existing
EBM approaches achieve strong discriminative performance, they are unable to
generate samples conditioned on novel attribute combinations. We propose a
simple extension which expands the capabilities of EBMs to generating accurate
conditional samples. Our approach, combined with newly developed techniques in
energy-based model training, allows us to directly maximize the likelihood of
data and labels under the unnormalized joint distribution. We evaluate our
proposed approach on high-dimensional image data with high-dimensional binary
attribute labels. We find our models are capable of both accurate, calibrated
predictions and high-quality conditional synthesis of novel attribute
combinations.
