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Auflistung nach Schlagwort "Generative models"

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  • Textdokument
    Radio Galaxy Classification with wGAN-Supported Augmentation
    (INFORMATIK 2022, 2022) Kummer,Janis; Rustige,Lennart; Griese,Florian; Borras,Kerstin; Brüggen,Marcus; Connor,Patrick L. S.; Gaede,Frank; Kasieczka,Gregor; Schleper,Peter
    Novel techniques are indispensable to process the flood of data from the new generation of radio telescopes. In particular, the classification of astronomical sources in images is challenging. Morphological classification of radio galaxies could be automated with deep learning models that require large sets of labelled training data. Here, we demonstrate the use of generative models, specifically Wasserstein GANs (wGAN), to generate artificial data for different classes of radio galaxies. Subsequently, we augment the training data with images from our wGAN. We find that a simple fully-connected neural network for classification can be improved significantly by including generated images into the training set.
  • Zeitschriftenartikel
    Robot in the Mirror: Toward an Embodied Computational Model of Mirror Self-Recognition
    (KI - Künstliche Intelligenz: Vol. 35, No. 1, 2021) Hoffmann, Matej; Wang, Shengzhi; Outrata, Vojtech; Alzueta, Elisabet; Lanillos, Pablo
    Self-recognition or self-awareness is a capacity attributed typically only to humans and few other species. The definitions of these concepts vary and little is known about the mechanisms behind them. However, there is a Turing test-like benchmark: the mirror self-recognition, which consists in covertly putting a mark on the face of the tested subject, placing her in front of a mirror, and observing the reactions. In this work, first, we provide a mechanistic decomposition, or process model, of what components are required to pass this test. Based on these, we provide suggestions for empirical research. In particular, in our view, the way the infants or animals reach for the mark should be studied in detail. Second, we develop a model to enable the humanoid robot Nao to pass the test. The core of our technical contribution is learning the appearance representation and visual novelty detection by means of learning the generative model of the face with deep auto-encoders and exploiting the prediction error. The mark is identified as a salient region on the face and reaching action is triggered, relying on a previously learned mapping to arm joint angles. The architecture is tested on two robots with completely different face.