1. Some confusions. In Parameter Transformation part, you state that “The number of adaptation parameters is given by k (2 d2 + d + 2). This is typically much smaller than the number of MDN parameters (weights and biases from all layers)”. In previous part you state that “The MDN output with all the mixture parameters has dimension p = k (d(d + 1)/2 + d + 1).” Why the adaptation parameters is much smaller than the number of MDN parameters? 2. Some figures are not self-explanatory. For instance, in Figure 4, the line of No adapt or Finetune are covered by other lines, without additional explanation. 3. More experiments. How the unsupervised domain adaptation performs based on the baseline model and how it compares with the proposed approach?
2. Some figures are not self-explanatory. For instance, in Figure 4, the line of No adapt or Finetune are covered by other lines, without additional explanation.