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The Impact of Noise on Generative and Discriminative Image ClassifiersStenlund, Maximilian, Jakobsson, Valdemar January 2022 (has links)
This report analyzes the difference between discriminative and generative image classifiers when tested on noise. The generative classifier was a maximum-likelihood based classifier using a normalizing flow as the generative model. In this work, a coupling flow such as RealNVP was used. For the discriminative classifier a convolutional network was implemented. A detailed description of how these classifiers were implemented is given in the report. The report shows how this generative classifier outperforms the discriminative classifier when tested on adversarial noise. However, tests are also conducted on salt and pepper noise and Gaussian noise, here the results show that the generative classifier gets outperformed by the discriminative classifier. Tests were also conducted on Gaussian noise once both classifiers had been trained on Gaussian noise, the results from these tests show that the discriminative classifier performs significantly better once trained on Gaussian noise. However, the generative classifier does only show marginal increases in performance and performs worse on clean data once trained on Gaussian noise. / Den här rapporten analyserar skillnaden mellan diskriminativa och generativa modellklasser för bildigenkänning när de testas på brus. Den generativa modellklassen var en maximum-likelihood baserad generativ klassifikationsmodell. Inom detta arbete användes kopplingsflödet RealNVP. För den diskriminativa bildigenkänningsmodellen så implementerades ett faltningsnätverk. En detaljerad beskrivning för hur dessa bildigenkänningsmodeller genomfördes är given i rapporten. Rapporten visar hur den generativa modellklassen överträffar den diskriminativa modellklassen när de testas på adversarialt brus. Testerna utförs emellertid med salt och peppar brus och Gaussiskt brus, för dessa visar resultaten att den generativa modellklassen överträffas av den diskriminativa modellklassen. Den generativa modellklassen visar emellertid endast marginella ökningar i prestanda, och har en sämre prestanda på ren data efter att den tränats på Gaussiskt brus. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm
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