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英语新闻阅读：怎样自拍更好看

Researchers have trained a neural network designed to think like the human brain to analyse millions of selfies to find what works.

Andrej Karpathy, a PhD student at Stanford working on Deep Learning, decided to use a powerful, 140-million-parameter state-of-the-art Convolutional Neural Network to help work out why some selfies are more popular than other.

'We decided to feed it 2 million selfies from the internet, and train it to classify good selfies from bad ones,' he explained in a blog.

The neural network, known as ConvNet 'looked' at every one of the 2 million selfies several tens of times.

Each time, it tuned its filters in a way that best allows it to separate good selfies from bad ones.

'We can't very easily inspect exactly what it found (it's all jumbled up in 140 million numbers that together define the filters),' said Karpathy.

'A few patterns stand out for me,' he said.

Women were consistently ranked higher than men - to the extent there was not a single male in the top 100.

The position and pose of the face is quite consistent among the top images.

The face always occupies about 1/3 of the image, is slightly tilted, and is positioned in the center and at the top.

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