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1 de ago. de 2021 · The DBCN model was built using the self-designed dilated-block architecture. Experiments demonstrated that the dilated-block design helps the DBCN model to identify rumors more accurately and optimizes learning curves. The DBCN model outperformed other baseline models in rumor identification and the early identification experiments. •
27 de nov. de 2023 · Deblur Capsule Networks is an end-to-end three-stage blind image deblurring convolutional neural network composed of blur type classification, PSF reconstruction, and deep regularized deconvolution modules, depicted in Fig. 1.Given an input blurry image \(\tilde{I} = k * I\), DbCN first encapsulates the blur kernel k in a vectorized encoded representation \(\boldsymbol{c}\) using a routing-by ...
1 de ene. de 2016 · These new features are summarized in this document. Packaged with MCNP6 is also the new production release of the ENDF/B-VII.1 nuclear data files usable by MCNP. The high quality of the overall merged code, usefulness of these new features, along with the desire in the user community to start using the merged code, have led us to make the first ...
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To apply our DBCN to the M&Ms-2 challenge we modified the nnUNet framework for biomedical image segmentation and replaced the lowest, or bottleneck, layer with a single deformable Bayesian convolution. We then trained two separate networks, one for LA images and one for SA images.
