If θ 1 is low, C t may get trained on incorrectly predicted pseudo labeled instances; whereas, if θ 1 is high, C t may be deficient of instances to learn a good decision boundary.
θ 2 influences the number of iterations required by the algorithm to reach the  Figure 4: Illustrates how the weight (w t ) for target domain classifiers varies for the most and least similar domains with number of iterations.