# On transfer learning using a MAC model variant

arXiv: https://arxiv.org/abs/1811.06529 · HTML: https://mlsanity.com/paper/1811.06529/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2018.

## Built on by (most cited first, 2 of 2)

- [Compositionality as Lexical Symmetry](https://mlsanity.com/paper/2201.12926/) (2022)
- [Visual Superordinate Abstraction for Robust Concept Learning](https://mlsanity.com/paper/2205.14444/) (2022)

## Abstract (arXiv)

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.