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During the late 1980s, "skip-layer" connections were sometimes used in neural networks. Examples include: [17] [18] Lang and Witbrock (1988) [19] trained a fully connected feedforward network where each layer skip-connects to all subsequent layers, like the later DenseNet (2016). In this work, the residual connection was the form () + (), where ...
The ResNet paper, [17] however, provided strong experimental evidence of the benefits of going deeper than 20 layers. It argued that the identity mapping without modulation is crucial and mentioned that modulation in the skip connection can still lead to vanishing signals in forward and backward propagation (Section 3 in [17]).
A bypass switch (or bypass TAP) is a hardware device that provides a fail-safe access port for an in-line active security appliance such as an intrusion prevention system (IPS), next generation firewall (NGFW), etc. Active, in-line security appliances are single points of failure in live computer networks because if the appliance loses power, experiences a software failure, or is taken off ...
U-Net is a convolutional neural network that was developed for image segmentation. [1] The network is based on a fully convolutional neural network [2] whose architecture was modified and extended to work with fewer training images and to yield more precise segmentation.
A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]
A reverse connection is usually used to bypass firewall restrictions on open ports. [1] A firewall usually blocks incoming connections on closed ports, but does not block outgoing traffic . In a normal forward connection, a client connects to a server through the server's open port , but in the case of a reverse connection, the client opens the ...
In a neural network, batch normalization is achieved through a normalization step that fixes the means and variances of each layer's inputs. Ideally, the normalization would be conducted over the entire training set, but to use this step jointly with stochastic optimization methods, it is impractical to use the global information.
Reference to a HetNet often indicates the use of multiple types of access nodes in a wireless network. A Wide Area Network can use some combination of macrocells, picocells, and femtocells in order to offer wireless coverage in an environment with a wide variety of wireless coverage zones, ranging from an open outdoor environment to office buildings, homes, and underground areas.