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+**Related To**
+Filter Groups
+DenseNet
+** References **
+https://arxiv.org/pdf/1611.07661v1.pdf  Neural Multigrid
+Rather than manipulating representations
+living on a single spatial grid, our network layers
+operate across scale space, on a pyramid of tensors. They
+consume multigrid inputs and produce multigrid outputs;
+convolutional filters themselves have both within-scale and
+cross-scale extent. This aspect is distinct from simple multiscale
+designs, which only process the input at different
+scales. Viewed in terms of information flow, a multigrid
+network passes messages across a spatial pyramid. As a
+consequence, receptive field size grows exponentially with
+depth, facilitating rapid integration of context. Most critically,
+multigrid structure enables networks to learn internal
+attention and dynamic routing mechanisms, and use them to
+accomplish tasks on which modern CNNs fail.   https://github.com/buttomnutstoast/Multigrid-Neural-Architectures
+https://arxiv.org/pdf/1512.02767v2.pdf  Affinity CNN: Learning Pixel-Centric Pairwise Relations for Figure/Ground Embedding
+http://redwood.berkeley.edu/vs265/olshausen-etal93.pdf A Neurobiological Model of Visual Attention and Invariant Pattern
+Recognition Based on Dynamic Routing of Information
+https://arxiv.org/pdf/1611.09326v1.pdf The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
+In this paper, we extend DenseNets to deal with the problem
+of semantic segmentation. We achieve state-of-the-art
+results on urban scene benchmark datasets such as CamVid
+and Gatech, without any further post-processing module
+nor pretraining. Moreover, due to smart construction of the
+model, our approach has much less parameters than currently
+published best entries for these datasets.
+{{:wiki:tiramisu.png|}}
+https://arxiv.org/pdf/1708.07038v1.pdf Non-linear Convolution Filters for CNN-based Learning
+Typical convolutional
+layers are linear systems, hence their expressiveness
+is limited. To overcome this, various non-linearities
+have been used as activation functions inside CNNs, while
+also many pooling strategies have been applied. We address
+the issue of developing a convolution method in the
+context of a computational model of the visual cortex, exploring
+quadratic forms through the Volterra kernels. Such
+forms, constituting a more rich function space, are used as
+approximations of the response profile of visual cells.
+The Volterra series model is a sequence of approximations
+for continuous functions, developed to represent the
+input-output relationship of non-linear dynamical systems,
+using a polynomial functional expansion. Their equations
+can be composed by terms of infinite orders, but practical
+implementations based on them use truncated versions, retaining
+the terms up to some order r.
+http://vcl.iti.gr/volterra-based-convolution-filter-implementation-in-torch/
+https://arxiv.org/pdf/1707.08308v1.pdf Tensor Regression Networks
+To date, most convolutional neural network architectures output predictions by
+flattening 3rd-order activation tensors, and applying fully-connected output layers.
+This approach has two drawbacks: (i) we lose rich, multi-modal structure during
+the flattening process and (ii) fully-connected layers require many parameters. We
+present the first attempt to circumvent these issues by expressing the output of a
+neural network directly as the the result of a multi-linear mapping from an activation
+tensor to the output. By imposing low-rank constraints on the regression tensor, we
+can efficiently solve problems for which existing solutions are badly parametrized.
+Our proposed tensor regression layer replaces flattening operations and fullyconnected
+layers by leveraging multi-modal structure in the data and expressing the
+regression weights via a low rank tensor decomposition. Additionally, we combine
+tensor regression with tensor contraction to further increase efficiency. Augmenting
+the VGG and ResNet architectures, we demonstrate large reductions in the number
+of parameters with negligible impact on performance on the ImageNet dataset.