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?