The above adversarial loss leads two different optimized directions for Θ g and Θ DeLexi , which can be implemented by a gradient reversal layer (Ganin et al., 2016) during backpropagation.
l minmax is jointly optimized with the main ED task objective l event , while gradients from adversarial loss are reversed with the factor λ adv when they reach r g .