895 lines
50 KiB
Clojure
895 lines
50 KiB
Clojure
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:content {:text "How May Deep Learning Testing Inform Model Generalizability? The Case of Image Classification"},
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:content {:text " Software Engineering for Artificial Intelligence."},
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:content {:text "e additional social and environmental factors come into play"},
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:content {:text "role of input testing as a early indicator of the real-world performance of deep learning models in the context of image recognition"},
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:content {:text "discover that the performance of the same model drastically drops when input testing is applied, hence suggesting that (1) the currently available models would not properly work in practice and (2) input testing may provide insights to machine learning engineers on the generalizability of the model in practice, hence possibly informing their design actions."},
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:page 5},
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:content {:text "Our preliminary results indicated that the application of input testing methods lets the performance of the CNN decrease up to 60% with respect to what reported in literature. The overall performance ranged, indeed, between 19% to 33% in terms of precision, recall, F-Measure, and accuracy."},
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:properties {:color "yellow"}}],
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:extra {:page 5}}
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