Meta-Query-Net: ResolvingPurity-InformativenessDilemmain Open-setActiveLearning (SupplementaryMaterial) ACompleteProofofTheorem4.1

Let g[1](zx) be g(zx) and W[1] be W for notation simplicity. Consider each dimension's scalar output ofg(zx), and it is denoted asg p (zx) where p is an index of the output dimension. For each AL round, a target modelΘis trained via stochastic gradient descent(SGD) using IN examples in the labeled setSL (Lines 3-5). The initial learning rate of0.1 is decayed by a factor of 0.1 at 50% and 75% of the total training iterations. Owing to the ability to find the best balance between purity and informativeness, MQ-Net achieves the highest accuracy on every AL round.