  In this paper, we propose energy-based sample adaptation at test time for
domain generalization. Where previous works adapt their models to target
domains, we adapt the unseen target samples to source-trained models. To this
end, we design a discriminative energy-based model, which is trained on source
domains to jointly model the conditional distribution for classification and
data distribution for sample adaptation. The model is optimized to
simultaneously learn a classifier and an energy function. To adapt target
samples to source distributions, we iteratively update the samples by energy
minimization with stochastic gradient Langevin dynamics. Moreover, to preserve
the categorical information in the sample during adaptation, we introduce a
categorical latent variable into the energy-based model. The latent variable is
learned from the original sample before adaptation by variational inference and
fixed as a condition to guide the sample update. Experiments on six benchmarks
for classification of images and microblog threads demonstrate the
effectiveness of our proposal.
