• 7月6日:Jean-Michel Morel/沈佐偉
    發布時間:2019-07-03  閱讀次數:2588

    報告一:Jean-Michel Morel

     

    報告題目:Is There a General Theory for the Detection of Anomalies in Images?
    報告人:   Jean-Michel Morel
    教授  法國加香高師
    主持人:   沈超敏 副教授
    報告時間:2019年7月6日  周六14:00-15:00 
    報告地點:理科大樓B1002

     

    報告摘要:
    Anomaly detectors address the difficult problem of detecting automatically exceptions in an arbitrary background image. Detection methods have been proposed by the thousands because each problem requires a different background model. By analysing key examples of the literature, we show that all anomaly detectors are characterized by their choice among seven fundamental principles guiding the background model and the decision method. We show that these principles can be combined in a general method that uses six of them. Our synthesis reduces the problem to the easier problem of detecting anomalies in noise. In that way, the varifold background modeling problem is replaced by simpler noise modeling, and allows the calculation of rigorous thresholds based on the a contrario detection theory. Our conclusion is that it is possible to perform automatic anomaly detection even on a single image. (Joint work of Thibaud Ehret, Axel Davy, Jean-Michel Morel, Mauricio Delbracio)

    報告人簡介:
    Jean-Michel Morel, professor at the École normale supérieure of Cachan. Mathematics Center and their applications, lauréat of the Grand Prix Inria - Academy of Sciences in 2013. Focusing on the analysis and mathematical processing of images, Prof Morel’s most notable contributions are in the areas of segmentation, denoising, mapping, and detecting significant events in digital images. Prof. Morel is the founder of the online scientific publication “Image Processing OnLine” (http://www.ipol.im/). He has won numerous prizes, including Philip Morris Mathematics Prize (1991), CISI-Engineering Award for Applied Mathematics (1992),Science and Defense Award (1996), INRIA Grand Prix - Academy of Sciences (2013), CNRS Medal of Innovation (2015) and IEEE CVPR Longuet-Higgins Prize (2015).

     

    報告二:沈佐偉

     

    報告題目:Deep Approximation via Deep Learing
    報告人:   沈佐偉  
    教授  新加坡國家科學院院士, 新加坡國立大學
    主持人:   沈超敏 副教授
    報告時間:2019年7月6日   周六15:00-16:00
    報告地點:理科大樓B1002

     

    報告摘要:
    The primary task of many applications is approximating/estimating a function through samples drawn from a probability distribution on the input space. The deep approximation is to approximate a function by compositions of many layers of simple functions, that can be viewed as a series of nested feature extractors. The key idea of deep learning network is to convert layers of compositions to layers of tunable parameters that can be adjusted through a learning process, so that it achieves a good approximation with respect to the input data. 
    In this talk, we shall discuss mathematical foundation behind this new approach of approximation; how it differs from the classic approximation theory, and how this new theory can be applied to understand and design deep learning network.

    報告人簡介:

    沈佐偉教授,新加坡國立大學理學院院長,陳振傳百年紀念教授,新加坡國家科學院院院士,美國數學會會士(AMS  Fellow), 美國工業與應用數學會會士(SIAM Fellow)。主要研究領域為逼近與小波理論、時頻分析、圖像科學, 學習理論等。作為國際著名數學家,沈佐偉教授先后獲得Wavelet Pioneer獎、新加坡國立大學杰出科學研究獎和新加坡科學成就獎,并受邀在2010年國際數學家大會和2015年國際工業與應用數學大會上作報告。

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