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Simplified neural network example for object detection: As seen at the output at right, the network is trained to associate a ringed texture and star outline with a starfish, and a striped texture and oval shape with a sea urchin. In this run, it correctly detects the starfish in the input picture at left. In addition, a shell that was not included in the training gives a weak signal for the oval shape, resulting in a weak signal from only one of two intermediate nodes for the sea urchin output, which may still result in a false positive result (or "hallucination") for sea urchin. In reality, textures and outlines would not be represented by single nodes, but rather by associated weight patterns of multiple nodes.
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Reference: Ferrie, C., & Kaiser, S. (2019) Neural Networks for Babies, Sourcebooks ISBN: 1492671207.
Simplified example of training a neural network in object detection: The network is trained by multiple images that are known to depict starfish and sea urchins, which are correlated with "nodes" that represent visual features. The starfish match with a ringed texture and a star outline, whereas most sea urchins match with a striped texture and oval shape. However, the instance of a ring textured sea urchin creates a weakly weighted association between them.
Subsequent run of the network on an input image (left): The network correctly detects the starfish. However, the weakly weighted association between ringed texture and sea urchin also confers a weak signal to the latter from one of two intermediate nodes. In addition, a shell that was not included in the training gives a weak signal for the oval shape, also resulting in a weak signal for the sea urchin output. These weak signals may result in a false positive result for sea urchin.
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Captions
Simplified neural network example: The network is trained to associate a ringed pattern and star outline with a sea star, and a striped pattern and oval shape with a sea urchin. In this run, it correctly detects the sea star in the picture at left.