File:Simplified neural network example.svg

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    Summary

    Description
    English: 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.
    Date
    Source

    Own work

    • Reference: Ferrie, C., & Kaiser, S. (2019) Neural Networks for Babies, Sourcebooks ISBN: 1492671207.
    Author
    Mikael Häggström, M.D. Author info
    - Reusing images
    - Conflicts of interest:
      None
    Mikael Häggström, M.D.
    Other versions
    Raster (.png) version

    Context

    • Simplified example of training a neural network in object detection: The network is trained by multiple images known to depict starfish and sea urchins, which are correlated with "nodes" representing visual features. Starfish match with a ringed texture and star outline, whereas most sea urchins match with a striped texture and oval shape.
      Simplified example of training a neural network in object detection: The network is trained by multiple images known to depict starfish and sea urchins, which are correlated with "nodes" representing visual features. Starfish match with a ringed texture and star outline, whereas most sea urchins match with a striped texture and oval shape.
    • The resulting model contains weighted associations between visual features and output categories. Because one training image depicts a ring-textured sea urchin, the model also develops a weak association between ringed texture and sea urchin.
      The resulting model contains weighted associations between visual features and output categories. Because one training image depicts a ring-textured sea urchin, the model also develops a weak association between ringed texture and sea urchin.
    • Subsequent run of the model on an input image (left): The network correctly detects the starfish. However, the weak association between ringed texture and sea urchin also gives a weak signal to the latter from one of two intermediate nodes. In addition, a shell 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.
      Subsequent run of the model on an input image (left): The network correctly detects the starfish. However, the weak association between ringed texture and sea urchin also gives a weak signal to the latter from one of two intermediate nodes. In addition, a shell 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.

    Licensing

    Creative Commons CC-Zero This file is made available under the Creative Commons CC0 1.0 Universal Public Domain Dedication.
    The person who associated a work with this deed has dedicated the work to the public domain by waiving all of their rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.

    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.

    Items portrayed in this file

    depicts

    TensorFlow

    inception

    21 September 2023

    File history

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    Date/TimeThumbnailDimensionsUserComment
    current23:52, 2 October 2023Thumbnail for version as of 23:52, 2 October 20231,028 × 882 (71 KB)wikimediacommons>Mikael HäggströmUpdate

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