Neuroscientists find a way to make object-recognition models perform better

Computer vision models known as convolutional neural networks can be trained to recognize objects nearly as accurately as humans do. However, these models have one significant flaw: Very small changes to an image, which would be nearly imperceptible to a human viewer, can trick them into making egregious errors such as classifying a cat as a tree.

from News on Artificial Intelligence and Machine Learning https://ift.tt/2Vtu0Yp
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