  We introduce a variant of the MAC model (Hudson and Manning, ICLR 2018) with
a simplified set of equations that achieves comparable accuracy, while training
faster. We evaluate both models on CLEVR and CoGenT, and show that, transfer
learning with fine-tuning results in a 15 point increase in accuracy, matching
the state of the art. Finally, in contrast, we demonstrate that improper
fine-tuning can actually reduce a model's accuracy as well.
