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A basic block is the simplest building block studied in the original ResNet. [1] This block consists of two sequential 3x3 convolutional layers and a residual connection. The input and output dimensions of both layers are equal. Block diagram of ResNet (2015). It shows a ResNet block with and without the 1x1 convolution.
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Inception [1] is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed Inception v1).). The series was historically important as an early CNN that separates the stem (data ingest), body (data processing), and head (prediction), an architectural design that persists in all modern
LeNet-5 architecture block diagram LeNet-5 architecture (detailed). LeNet-5 is similar to LeNet-4, but with more fully connected layers. Its architecture is shown in the image on the right. It has 2 convolutions, 2 average poolings, and 3 fully connected layers. LeNet-5 was trained for about 20 epoches over MNIST.
A block diagram is a diagram of a system in which the principal parts or functions are represented by blocks connected by lines that show the relationships of the blocks. [1] They are heavily used in engineering in hardware design , electronic design , software design , and process flow diagrams .
Bifurcation diagram of the one-neuron recurrent network. Horizontal axis is b, and vertical axis is x. The black curve is the set of stable and unstable equilibria. Notice that the system exhibits hysteresis, and can be used as a one-bit memory.
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Two techniques were developed concurrently to train very deep networks: highway network, [108] and the residual neural network (ResNet). [109] They allowed over 1000-layers-deep networks to be trained.