3 ms·
>> another 2% improvement 2% is HUGE at this point, at least on the datasets that I am familiar with - ImageNet, MS-Coco, PascalVOC etc. And at this point, any
by niyazpk 9y ago
>> another 2% improvement
2% is HUGE at this point, at least on the datasets that I am familiar with - ImageNet, MS-Coco, PascalVOC etc. And at this point, any modifications or strategies that gets you the 2% improvement is noteworthy, and I know that people in my team are looking forwards to techniques that will give us these improvements.
- skierscott 9y ago> 2% is HUGE at this point Hell, on MNIST 0.14% is huge. Geff Hinton created an entire new architecture to get 0.25% error, which is far better than the baseline 0.39% error [1]. To be fair, he did reduce the error by 35%. [1]:https://arxiv.org/pdf/1710.09829.pdf https://arxiv.org/pdf/1710.09829.pdf
- sytelus 9y agoExactly my point. The "huge" improvement is perceived as important although has no real impact on advancing the field and solving core real issues.
- dontreact 9y agoAgreed. In medical applications a tiny "incremental" advance can mean the difference between worse than human and superhuman performance, which could mean revolutionary changes to clinical care.