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Multi-attribute classification generalizes classification, presenting newchallenges for making accurate predictions and quantifying uncertainty. Webuild upon recent work and show that architectures for multi-attributeprediction can be reinterpreted as energy-based models (EBMs). While existingEBM approaches achieve strong discriminative performance, they are unable togenerate samples conditioned on novel attribute combinations. We propose asimple extension which expands the capabilities of EBMs to generating accurateconditional samples. Our approach, combined with newly developed techniques inenergy-based model training, allows us to directly maximize the likelihood ofdata and labels under the unnormalized joint distribution. We evaluate ourproposed approach on high-dimensional image data with high-dimensional binaryattribute labels. We find our models are capable of both accurate, calibratedpredictions and high-quality conditional synthesis of novel attributecombinations.

Directly Training Joint Energy-Based Models for Conditional Synthesis and Calibrated Prediction of Multi-Attribute Data