Today, state-of-the-art visual recognition systems are trained using large datasets of annotated images produced by humans. Acquiring this data is a costly and time-consuming process,
To solve this problem Alphabet’s DeepMind company is introducing the Generative Query Network (GQN), a framework within which machines learn to perceive their surroundings by training only on data obtained by themselves as they move around scenes. Much like infants and animals, the GQN learns by trying to make sense of its observations of the world around without any human labelling of the contents of scenes.
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