Convolutional Neural Fabrics Convolutional Neural Fabrics
Paper summary Convolutional Neural Fabrics (CNFs) are a construction algorithm for CNN architectures. > Instead of aiming to select a single optimal architecture, we propose a “fabric” that embeds an exponentially large number of architectures. The fabric consists of a 3D trellis that connects response maps at different layers, scales, and channels with a sparse homogeneous local connectivity pattern. ![Image](http://i.imgur.com/wlISXgo.png) * **Pooling**: CNFs don't use pooling. However, this might not be necessary as they use strided convolution. * **Filter size**: All convolutions use kernel size 3. * **Output layer**: Scale $1 \times 1$, channels = nr of classes * **Activation function**: Rectified linear units (ReLUs) are used at all nodes. ## Evaluation * Part Labels dataset (face images from the LFW dataset): a super-pixel accuracy of 95.6% * MNIST: 0.33% error (see [SotA](https://martin-thoma.com/sota/#image-classification); 0.21 %) * CIFAR10: 7.43% error (see [SotA](https://martin-thoma.com/sota/#image-classification); 2.72 %) ## What I didn't understand * "Activations are thus a linear function over multi-dimensional neighborhoods, i.e. a four dimensional 3×3×3×3 neighborhood when processing 2D images" * "within the first layer, channel c at scale s receives input from channels c + {−1, 0, 1} from scale s − 1": Why does the scale change? Why doesn't the first layer receive input from the same scale?
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Convolutional Neural Fabrics
Shreyas Saxena and Jakob Verbeek
arXiv e-Print archive - 2016 via arXiv
Keywords: cs.CV, cs.LG, cs.NE

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