Collaborating Authors teams with Prada on exclusive capsule

Los Angeles Times

Munich-based retailer is continuing to foster its close partnership with Prada, with the launch of an exclusive capsule collection. The range builds on the success of the Italian house's feather-trimmed pieces from spring, with a ready-to-wear and accessories range that focuses on delicate embellishments of all sorts. There are floral-printed midi dresses, jackets and tops often embellished with pastel-hued feathers, as well as a selection of Sixties-inspired, bright A-line dresses featuring sequined embroideries on the collar. The capsule also includes a range of accessories, such as feather-trimmed mules in shades of black, gray and bright fuchsia, embroidered loafers, leather gloves and a cross-body bag with feathers scattered all over the strap. Prices range from 510 pounds, or $660, for a pair of feather mules to 1,675 pounds, or $2,166, for a sequined dress.

r/deeplearning - Number of capsules in the Primary Capsule Layer of Capsule networks


In hintons paper the primary capsule is directly connected to the final class capsule layer, therefore the number of capsule's in the primary layers can be viewed as being 32 6 6 with the 6 6 sharing the same W( the 8 16 matrix which converts them to class capsule). You can arrive at the 32 6 6 figure by looking at the routing algorithm( Page 3) there the number capsules in layer L and layer L 1 is'i' and'j' respectively, if you look at the implemrntations the Bij matrix is a (32 6 6,10) matrix implying that'i' is 32 6 6 and'j' is 10. Certain implementations have Bij as being a (32 6 6,10,8) matrix where each element of the 8D output is multiplied by a different value.

Capsule Networks


Abstract: A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or object part. We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters. When multiple predictions agree, a higher level capsule becomes active. We show that a discriminatively trained, multi-layer capsule system achieves state-of-the-art performance on MNIST and is considerably better than a convolutional net at recognizing highly overlapping digits. To achieve these results we use an iterative routing-by-agreement mechanism: A lower-level capsule prefers to send its output to higher level capsules whose activity vectors have a big scalar product with the prediction coming from the lower-level capsule.

Blue Peter capsule dug up by accident

BBC News

A Blue Peter time capsule has been accidently dug up by construction workers 33 years earlier than planned. The Millennium Time Capsule was buried under the Millennium Dome, now the O2 Arena, in 1998. Filled with viewers' mementos of the time, it was not supposed to be unearthed until 2050. The O2 has said despite being damaged, the capsule's contents are safe. The BBC said the capsule will be re-buried.

Understanding Capsule Networks with Its Implementation in Computer Vision


After this, we define the digit capsule which is a total of 10 in number that outputs 16-dimensional vectors. Let us see how to compute this digit capsule. So, the first step is to compute the predicted output vectors since the second layer is connected to the first layer and we will predict one output for each pair of first and second layer capsules. By making use of the first primary capsule we can predict the output of the first digit capsule. This is done for all the digit capsules using the first primary capsule.