Исторически значимая AI-модель, установившая рекорд точности на бенчмарке MNIST с уровнем ошибки всего 0,27%. Использование комитета из семи нейросетей и мощностей графических процессоров позволило ИИ вплотную приблизиться к возможностям человеческого зрения в распознавании цифр.
In 2010, after many years of stagnation, the MNIST handwriting recognition benchmark record dropped from 0.40% error rate to 0.35%. Here we report 0.27% for a committee of seven deep CNNs trained on graphics cards, narrowing the gap to human performance. We also apply the same architecture to NIST SD 19, a more challenging dataset including lower and upper case letters. A committee of seven CNNs obtains the best results published so far for both NIST digits and NIST letters. The robustness of our method is verified by analyzing 78125 different 7-net committees.