Structured Output Learning with Conditional Generative Flows

Understand

Traditional structured prediction models try to learn the conditional likelihood, i.e., p(y|x), to capture the relationship between the structured output y and the input features x.

• For many models, computing the likelihood is intractable.
• These models are therefore hard to train, requiring the use of surrogate objectives or variational inference to approximate likelihood.
• In this paper, we propose conditional Glow (c-Glow), a conditional generative flow for structured output learning.