Comments (5)
"As this work is still in progress, these are preliminary results evaluated on grids up to 10×10."
I guess this 80% is only on the grids of small sizes, which are disproportionately the "easy" tasks involving mere rotation/mirroring.
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Has anyone tried implementing this paper? I can't find a working demonstration anywhere.
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I think there are several strange things about their paper.
- In a later presentation (2021) they say "NAR achieves 61.13% accuracy on the Abstraction and Reasoning Corpus" with no further explanation. Is that measure on the entire dataset (i.e. all grid sizes)? Otherwise, why is number different? They use the exact same graphs as motivation in their poster as in the old article, yet with different numbers as the result, see: https://eucys2021.usal.es/computing-03-2021/
- As far as I understand it they evaluate on the public test set, yet they compare it to the Kaggle competition which ran on completely different, hidden, tasks.
- They claim to solve 78,8% of 100 hidden tasks but don't explain how they get .8 when the test are binary.
- There is no discussion on what the impact is when excluding all larger grids, this is especially relevant when they are comparing agains the Kaggle competition.
- There is no source code, no one (AFAIK) has reproduced the results, and there is no official benchmark against the hidden test set.
It's possible they have devised an approach that is better than the previous state-of-the-art, but at this point I find it hard to take their numbers at face value.
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Related Issues (20)
- 79fb03f4 test is unsolvable, water flow HOT 5
- b4a43f3b test is unsolvable, missing 2 green pixels HOT 1
- inquiry about the ARC data generation process HOT 1
- 05a7bcf2 has mistake in example output HOT 4
- 0d3d703e - not sure... (spoiler) HOT 1
- Color-blind support HOT 5
- ac0c5833 - 3rd training pair seems wrong HOT 1
- ac0c5833.json is in both training and evaluation HOT 1
- Bug in e1d2900e HOT 6
- dd2401ed mistake HOT 1
- A8610EF7.json has mistake in test output HOT 1
- Maintaining ARC HOT 1
- Please allow to go to test input 1/2, after going to test input 2/2
- Add URL to the playground HOT 3
- 4852f2fa train output - move one pixel HOT 1
- 0d87d2a6 is ambiguous HOT 1
- c35c1b4c train output 3 is asymmetric HOT 1
- b230c067 make less ambiguous, so it doesn't require 2 attempts HOT 1
- 423a55dc test is unsolvable, skew HOT 1
- 310f3251 test is unsolvable, wrap around HOT 3
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