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        <div id="content-top1">Page 104 - The Black Box Book</div>
        <div id="content-top2">P. 104</div>
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    <pre style="white-space:pre-line"><code>ATTEMPT 2: COMMON SENSE                                                                                ral networks pre-trained on a freely available image dataset (ImageNet)
      Considering the results of the first experiment, a method of content                                   containing images aggregated from photo-sharing sites (Flickr.com, etc.),
      analysis of the dataset was chosen, which suppresses the value of                                      thus significantly weakening the software’s credibility as a non-human
      the colour of the images by converting them into grayscale. Thus,                                      entity. The software functioned as a tool to search for relationships be-
      the images were represented by only one channel, with the values of each                               tween the images from the coronavirus pandemic collected in our Black
      pixel indicating the grayscale. The features were extracted from the images                            box and the images from ImageNet. Since the Black Box dataset is tiny
      and thus modified in the same way as in the first experiment, and again                                compared to ImageNet, the neural networks placed more emphasis on
      dimensionality reduction followed. The goal was to program the software                                those visual features identical to ImageNet content when sorting our da-
      to classify the dataset into unspecified categories automatically. For                                 taset. As a result, the arrangement of the photographs remained largely
      this purpose, several commonly used clustering algorithms were tested,                                 the same, allowing people, for instance, to mistake selfie images with and
      which work on the principle of comparing distances between points in                                   without a mask. When sorting the images, this made the layout of them
      the plane, focusing on finding separate clusters within the spatial projection of                      a more critical factor than the mask itself, the quarantine symbol. In this
      the dataset. Figure 7 shows the output of the Mean Shift algorithm                                     context, the pandemic experience appeared as a banal episode floating
      (Jin et al., 2017, p. 806–808), which we evaluated as the most suitable                                on the surface of the ocean of the visual overproduction of contemporary
      for our purposes.                                                                                      visual culture.
            In this case, the artificial neural networks classified the dataset
      according to thematic headings with greater or lesser accuracy across
      the supplied digital image sets. However, we were still working with neu-


             Figure 7: Thematic clusters
        obtained by extracting image parameters
        using a pre-trained neural network and
        the Mean Shift algorithm.
        Author: Štěpán Miklánek







































                                            104                                                                                              105</code></pre>
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