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  1. Book ; Online: Rethinking Style Transfer

    Kotovenko, Dmytro / Wright, Matthias / Heimbrecht, Arthur / Ommer, Björn

    From Pixels to Parameterized Brushstrokes

    2021  

    Abstract: There have been many successful implementations of neural style transfer in recent years. In most of these works, the stylization process is confined to the pixel domain. However, we argue that this representation is unnatural because paintings usually ... ...

    Abstract There have been many successful implementations of neural style transfer in recent years. In most of these works, the stylization process is confined to the pixel domain. However, we argue that this representation is unnatural because paintings usually consist of brushstrokes rather than pixels. We propose a method to stylize images by optimizing parameterized brushstrokes instead of pixels and further introduce a simple differentiable rendering mechanism. Our approach significantly improves visual quality and enables additional control over the stylization process such as controlling the flow of brushstrokes through user input. We provide qualitative and quantitative evaluations that show the efficacy of the proposed parameterized representation.

    Comment: Accepted at CVPR2021
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence ; Computer Science - Graphics
    Publishing date 2021-03-31
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article: Demonstrating Advantages of Neuromorphic Computation: A Pilot Study.

    Wunderlich, Timo / Kungl, Akos F / Müller, Eric / Hartel, Andreas / Stradmann, Yannik / Aamir, Syed Ahmed / Grübl, Andreas / Heimbrecht, Arthur / Schreiber, Korbinian / Stöckel, David / Pehle, Christian / Billaudelle, Sebastian / Kiene, Gerd / Mauch, Christian / Schemmel, Johannes / Meier, Karlheinz / Petrovici, Mihai A

    Frontiers in neuroscience

    2019  Volume 13, Page(s) 260

    Abstract: Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We employ a ... ...

    Abstract Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic system to implement a proof-of-concept demonstration of reward-modulated spike-timing-dependent plasticity in a spiking network that learns to play a simplified version of the Pong video game by smooth pursuit. This system combines an electronic mixed-signal substrate for emulating neuron and synapse dynamics with an embedded digital processor for on-chip learning, which in this work also serves to simulate the virtual environment and learning agent. The analog emulation of neuronal membrane dynamics enables a 1000-fold acceleration with respect to biological real-time, with the entire chip operating on a power budget of 57 mW. Compared to an equivalent simulation using state-of-the-art software, the on-chip emulation is at least one order of magnitude faster and three orders of magnitude more energy-efficient. We demonstrate how on-chip learning can mitigate the effects of fixed-pattern noise, which is unavoidable in analog substrates, while making use of temporal variability for action exploration. Learning compensates imperfections of the physical substrate, as manifested in neuronal parameter variability, by adapting synaptic weights to match respective excitability of individual neurons.
    Language English
    Publishing date 2019-03-26
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2411902-7
    ISSN 1662-453X ; 1662-4548
    ISSN (online) 1662-453X
    ISSN 1662-4548
    DOI 10.3389/fnins.2019.00260
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: Demonstrating Advantages of Neuromorphic Computation

    Wunderlich, Timo / Kungl, Akos F. / Müller, Eric / Hartel, Andreas / Stradmann, Yannik / Aamir, Syed Ahmed / Grübl, Andreas / Heimbrecht, Arthur / Schreiber, Korbinian / Stöckel, David / Pehle, Christian / Billaudelle, Sebastian / Kiene, Gerd / Mauch, Christian / Schemmel, Johannes / Meier, Karlheinz / Petrovici, Mihai A.

    A Pilot Study

    2018  

    Abstract: Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We employ a ... ...

    Abstract Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic system to implement a proof-of-concept demonstration of reward-modulated spike-timing-dependent plasticity in a spiking network that learns to play the Pong video game by smooth pursuit. This system combines an electronic mixed-signal substrate for emulating neuron and synapse dynamics with an embedded digital processor for on-chip learning, which in this work also serves to simulate the virtual environment and learning agent. The analog emulation of neuronal membrane dynamics enables a 1000-fold acceleration with respect to biological real-time, with the entire chip operating on a power budget of 57mW. Compared to an equivalent simulation using state-of-the-art software, the on-chip emulation is at least one order of magnitude faster and three orders of magnitude more energy-efficient. We demonstrate how on-chip learning can mitigate the effects of fixed-pattern noise, which is unavoidable in analog substrates, while making use of temporal variability for action exploration. Learning compensates imperfections of the physical substrate, as manifested in neuronal parameter variability, by adapting synaptic weights to match respective excitability of individual neurons.

    Comment: Added measurements with noise in NEST simulation, add notice about journal publication. Frontiers in Neuromorphic Engineering (2019)
    Keywords Computer Science - Neural and Evolutionary Computing ; Computer Science - Emerging Technologies
    Subject code 006
    Publishing date 2018-11-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  4. Book ; Online: Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate

    Billaudelle, Sebastian / Stradmann, Yannik / Schreiber, Korbinian / Cramer, Benjamin / Baumbach, Andreas / Dold, Dominik / Göltz, Julian / Kungl, Akos F. / Wunderlich, Timo C. / Hartel, Andreas / Müller, Eric / Breitwieser, Oliver / Mauch, Christian / Kleider, Mitja / Grübl, Andreas / Stöckel, David / Pehle, Christian / Heimbrecht, Arthur / Spilger, Philipp /
    Kiene, Gerd / Karasenko, Vitali / Senn, Walter / Petrovici, Mihai A. / Schemmel, Johannes / Meier, Karlheinz

    2019  

    Abstract: We present first experimental results on the novel BrainScaleS-2 neuromorphic architecture based on an analog neuro-synaptic core and augmented by embedded microprocessors for complex plasticity and experiment control. The high acceleration factor of ... ...

    Abstract We present first experimental results on the novel BrainScaleS-2 neuromorphic architecture based on an analog neuro-synaptic core and augmented by embedded microprocessors for complex plasticity and experiment control. The high acceleration factor of 1000 compared to biological dynamics enables the execution of computationally expensive tasks, by allowing the fast emulation of long-duration experiments or rapid iteration over many consecutive trials. The flexibility of our architecture is demonstrated in a suite of five distinct experiments, which emphasize different aspects of the BrainScaleS-2 system.
    Keywords Quantitative Biology - Neurons and Cognition ; Computer Science - Neural and Evolutionary Computing
    Publishing date 2019-12-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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