As I understand from the code, instead if using the Domain Factor network for weighting the memory value as explained in the paper, you train a "Concept Selector" and a "Domain Factor Selector" linear networks to output weights for both the memory and the domain factor net output to get the final v_transfer value when combing with the direct encoder output.
1) Can you please explain the motivation behind this change?
2) Can you explain the motivation behind training Man network before the domain factor network and not training both concept and domain factor selectors at once? (and in short, why not remove step 2)
3) Can you explain how these changes are applied in the semantic segmentation task training?
As I understand from the code, instead if using the Domain Factor network for weighting the memory value as explained in the paper, you train a "Concept Selector" and a "Domain Factor Selector" linear networks to output weights for both the memory and the domain factor net output to get the final v_transfer value when combing with the direct encoder output.
1) Can you please explain the motivation behind this change? 2) Can you explain the motivation behind training Man network before the domain factor network and not training both concept and domain factor selectors at once? (and in short, why not remove step 2) 3) Can you explain how these changes are applied in the semantic segmentation task training?
Thanks.