ROBOTIC PROCESS AUTOMATION

                                  CONVOLUTION NETWORKS

In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery.

The name “convolutional neural network” indicates that the network employs a mathematical operation called convolution.

“A convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data.”

ConvNext- The Return of Convolution Networks

Although back-propagation trained convolutional neural networks (ConvNets) date all the way back to the 1980s, it was not until the 2010s that we saw their true potential. The decade was marked by tremendous growth and the impact of deep learning. One of the primary drivers for the ‘renaissance of neural networks’ was convolution networks. Over the decade, the field of computer vision went through a paradigm shift. Engineering features shifted to designing architectures.

Convolutional networks were inspired by biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex. Individual cortical neurons respond to stimuli only in a restricted region of the visual field known as the receptive field. The receptive fields of different neurons partially overlap such that they cover the entire visual field.

CNNs use relatively little pre-processing compared to other image classification algorithms. This means that the network learns to optimize the filters (or kernels) through automated learning, whereas in traditional algorithms these filters are hand-engineered. This independence from prior knowledge and human intervention in feature extraction is a major advantage. CNNs are often compared to the way the brain achieves vision processing in living organisms.

A convolutional neural network consists of an input layer, hidden layers and an output layer. In any feed-forward neural network, any middle layers are called hidden because their inputs and outputs are masked by the activation function and final convolution. In a convolutional neural network, the hidden layers include layers that perform convolutions. Typically this includes a layer that performs a dot product of the convolution kernel with the layer’s input matrix. This product is usually the Frobenius inner product, and its activation function is commonly ReLU. As the convolution kernel slides along the input matrix for the layer, the convolution operation generates a feature map, which in turn contributes to the input of the next layer. This is followed by other layers such as pooling layers, fully connected layers, and normalization layers.

End-to-end training and prediction are common practice in computer vision. However, human interpretable explanations are required for critical systems such as self-driving cars. With recent advances in visual salience, spatial attention, and temporal attention, the most critical spatial regions/temporal instants could be visualized to justify the CNN predictions

ConvNeXt maintains the efficiency of standard ConvNets, and the fully-convolutional nature for both training and testing. This makes it extremely simple to implement. The creators of ConvNeXt hope that “the new observations and discussions can challenge some common beliefs and encourage people to rethink the importance of convolutions in computer vision”.