منابع مشابه
Learning Separable Filters with Shared Parts
Learned image features can provide great accuracy in many Computer Vision tasks. However, when the convolution filters used to learn image features are numerous and not separable, feature extraction becomes computationally demanding and impractical to use in real-world situations. In this thesis work, a method for learning a small number of separable filters to approximate an arbitrary non-sepa...
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Learning of convolutional filters in deep neural networks proves high efficiency to provide sparse representations for the purpose of image recognition. The computational cost of these networks can be alleviated by focusing on separable filters to reduce the number of learning parameters. Autoencoders are a family of powerful deep networks to build scalable generative models for automatic featu...
متن کاملSupplemental Material for the paper “ Learning Separable Filters
Figure 1 illustrates some filter banks learned on the DRIVE dataset. In particular, it shows how an example learned filter bank can be replaced by its rank-1 approximation obtained using the SVD decomposition (SEP-SVD). Figure 2 shows examples of 3D filter banks learned on the OPF dataset. In Figure 3 the central slice of the filter banks are given for a better comparison. We report the detaile...
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Poor eeciency is a typical problem of nonlinear diiusion ltering, when the simple and popular explicit (Euler-forward) scheme is used: for stability reasons very small time step sizes are necessary. In order to overcome this shortcoming, a novel type of semi-implicit schemes is studied, so-called additive operator splitting (AOS) methods. They share the advantages of explicit and (semi-)implici...
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ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2015
ISSN: 0162-8828,2160-9292
DOI: 10.1109/tpami.2014.2343229