A Survey on the New Generation of Deep Learning in Image Processing <s> 3) CapsuleNet <s> There are many innovations in imaging sensors designs. A bottleneck in evaluating proposed designs is the need to create optimal processing algorithms. I describe a pipeline architecture and simulation methods that automate the pipeline design. Article not available. <s> BIB001 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> 3) CapsuleNet <s> Our society will face a notable demographic shift in the near future. According to a United Nations report, the ratio of the elderly population (aged 60 years or older) to the overall population increased from 9.2% in 1990 to 11.7% in 2013 and is expected to reach 21.1% by 2050 [1]. According to the same report, 40% of older people live independently in their own homes. This ratio is about 75% in the developed countries. These facts will result in many societal challenges as well as changes in the health-care system, such as an increase in diseases and health-care costs, a shortage of caregivers, and a rise in the number of individuals unable to live independently [2]. Thus, it is imperative to develop ambient intelligence-based assisted living (AL) tools that help elderly people live independently in their homes. The recent developments in sensor technology and decreasing sensor costs have made the deployment of various sensors in various combinations viable, including static setups as well as wearable sensors. This article presents a survey that concentrates on the signal processing methods employed with different types of sensors. The types of sensors covered are pyro-electric infrared (PIR) and vibration sensors, accelerometers, cameras, depth sensors, and microphones. <s> BIB002 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> 3) CapsuleNet <s> 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 an 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. Active capsules at one level make predictions, via transformation matrices, for the instantiation parameters of higher-level capsules. When multiple predictions agree, a higher level capsule becomes active. We show that a discrimininatively 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. <s> BIB003
To breakthrough of scalar input and scalar output restrictions for traditional neurons, an innovative capsule unit was proposed that can output an activity vector represents the instantiation parameters of a specific type of entity such as an object or an object part, which can regard as vector version of neurons. Naturally, each layer of the CapsuleNet BIB003 is made up of some capsules; its main advantage is to make full use of spatial relations of data. To keep a clear understanding of the CapsuleNet architecture, a simple CapsuleNet architecture which based on traditional CNN shows in Fig.21 , and this architecture is relatively shallow with only two convolutional layers and one fully-connected layer. Assume the input x ∈ R 28×28 , and the label y ∈ R 10 . Conv1 has 256 convolution kernels ( 9 × 9) with a stride of 1 and ReLU activation. Therefore, the output of conv1 can express as, where W BIB001 i ∈ R 9×9 denotes i-th convolution kernels of first hidden layer and i = 1, 2, · · · , 256. Further, the second layer (or Primary Capsules) is a convolutional capsule layer with 4 primary capsules, and each primary capsule contains eight convolutional units with a 9 × 9 kernel and a stride of 2, that is, where W BIB002 j,s,k ∈ R 9×9 , and subscript k denotes convolution kernels, s means convolutional units and j represents primary capsules. In addition, s = 1, 2, · · · , 8 and j = 1, 2, 3, 4. For simplicity, then the output of the second layer can be rewritten as, h BIB002 ∈ R 4×6×6×8 Meanwhile, primary capsules of the second layer can be viewed as 4 × 6 × 6 capsules, and each capsule is an 8-dimension vector. Without loss of generality, for each capsule, we have, where s = j × u × v, and u, v = 1, 2, · · · , 6. Notably, no routing is used between Conv1 and primary capsules. Further, considering the number of categories is 10 for MNIST, so the number of digit capsules is 10. Concretely, where Squash(·) is a non-linear function to ensure that short vectors get shrunk to almost zero length and long vectors get shrunk to a length slightly below 1, and r means the r-th category, r = 1, 2, · · · , 10. Specially, weight matrix W (3) r,s ∈ R 16×8 , and the parameter C r,s ∈ R is coupling coefficient that are determined by the iterative dynamic routing process. To allow for multiple digits, we use a separate margin loss, where T r = 1 iff a digit of class k is present, and the parameter m + = 0.9, m − = 0.1. Finally, for the input x, the total loss is simply the sum of the losses of all digit capsules, that is, During the training phase, for all input samples {x n } N n=1 , then we can solve the correspond parameter by the optimization loss function n L(x n ). For the testing phase, we can takeŷ(r) equals L r (x), then the final predictionŷ = [ŷ(1), · · · ,ŷ(10)]. Undoubtedly, a simple capsules system already gives unparalleled performance at segmenting overlapping digits is an early indication that CapsuleNet are a direction worth exploring.