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Nirenberg Neuroscience (NN) develops machine vision on a principle that allows easy generalization – that is, it allows machine vision tasks such as navigation, collision avoidance, object recognition, and face recognition, to be performed in a broad range of conditions (lighting conditions, environments). NN’s method has a low footprint: the learning algorithms are shallow compared to deep learning networks, using many, many fewer operations to achieve very high performance (e.g., 1000-fold fewer for several applications such as face feature detection, pedestrian detection, and navigation).
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