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Wyszukujesz frazę "konwolucyjne sieci neuronowe" wg kryterium: Temat

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    Wyświetlanie 1-2 z 2
    Tytuł:
    On training deep neural networks using a streaming approach
    Autorzy:
    Duda, Piotr
    Jaworski, Maciej
    Cader, Andrzej
    Wang, Lipo
    Powiązania:
    https://bibliotekanauki.pl/articles/91796.pdf
    Data publikacji:
    2020
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    deep learning
    data streams
    convolutional neural networks
    strumienie danych
    konwolucyjne sieci neuronowe
    Opis:
    In recent years, many deep learning methods, allowed for a significant improvement of systems based on artificial intelligence methods. Their effectiveness results from an ability to analyze large labeled datasets. The price for such high accuracy is the long training time, necessary to process such large amounts of data. On the other hand, along with the increase in the number of collected data, the field of data stream analysis was developed. It enables to process data immediately, with no need to store them. In this work, we decided to take advantage of the benefits of data streaming in order to accelerate the training of deep neural networks. The work includes an analysis of two approaches to network learning, presented on the background of traditional stochastic and batch-based methods.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 1; 15-26
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A strong and efficient baseline for vehicle re-identification using deep triplet embedding
    Autorzy:
    Kumar, Ratnesh
    Weill, Edwin
    Aghdasi, Farzin
    Sriram, Parthasarathy
    Powiązania:
    https://bibliotekanauki.pl/articles/91741.pdf
    Data publikacji:
    2020
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    convolutional neural networks
    re-identification
    triplet networks
    siamese networks
    embedding
    hard data mining
    contrastive loss
    konwolucyjne sieci neuronowe
    sieci triplet
    sieci syjamskie
    osadzanie
    eksploracja danych
    Opis:
    In this paper we tackle the problem of vehicle re-identification in a camera network utilizing triplet embeddings. Re-identification is the problem of matching appearances of objects across different cameras. With the proliferation of surveillance cameras enabling smart and safer cities, there is an ever-increasing need to re-identify vehicles across cameras. Typical challenges arising in smart city scenarios include variations of viewpoints, illumination and self occlusions. Most successful approaches for re-identification involve (deep) learning an embedding space such that the vehicles of same identities are projected closer to one another, compared to the vehicles representing different identities. Popular loss functions for learning an embedding (space) include contrastive or triplet loss. In this paper we provide an extensive evaluation of triplet loss applied to vehicle re-identification and demonstrate that using the recently proposed sampling approaches for mining informative data points outperform most of the existing state-of-the-art approaches for vehicle re-identification. Compared to most existing state-of-the-art approaches, our approach is simpler and more straightforward for training utilizing only identity-level annotations, along with one of the smallest published embedding dimensions for efficient inference. Furthermore in this work we introduce a formal evaluation of a triplet sampling variant (batch sample) into the re-identification literature. In addition to the conference version [24], this submission adds extensive experiments on new released datasets, cross domain evaluations and ablation studies.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 1; 27-45
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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