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

• URL: http://arxiv.org/abs/2108.04227v1
• Date: Mon, 19 Jul 2021 22:19:41 GMT
• Title: Directly Training Joint Energy-Based Models for Conditional Synthesis and Calibrated Prediction of Multi-Attribute Data
• Authors: Jacob Kelly, Richard Zemel, Will Grathwohl
• Abstract summary: We show that architectures for multi-attribute prediction can be reinterpreted as energy-based models. We propose a simple extension which expands the capabilities of EBMs to generate accurate conditional samples. We find our models are capable of both accurate, calibrated predictions and high-quality conditional synthesis of novel attribute combinations.
• Score: 9.389098132764431
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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