using neural networks

Full Title

Arrangements for image or video recognition or understanding > using pattern recognition or machine learning > using neural networks

Classification Context

Section:

PHYSICS

Class:

COMPUTING OR CALCULATING; COUNTING

Subclass:

IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

Scope Notes

Glossary: AE auto-encoder network AlexNet CNN designed by Alex Krizhevsky et al. Backprop backpropagation, an algorithm for computing the gradient of the weights of an artificial neural network BERT bidirectional encoder representations from transformers, a transformer based artificial neural network CNN convolutional neural network, an artificial neural network that includes convolutional layers DNN deep neural network FCL fully connected layer of an artificial neural network FCNN fully convolutional neural network GAN generative adversarial network GoogLeNet deep convolutional neural network Inception convolutional neural network which concatenates several filters of different sizes at the same level of the network LeNet early CNN that firstly demonstrated the performance of CNNs on handwritten character recognition LSTM long short-term memory, a recurrent neural network MLP multi-layer perceptron MS COCO annotated image dataset Perceptron simple feed-forward neural network RBF radial basis function R-CNN convolutional neural network using a region proposal algorithm for object detection (variants: fast R-CNN, faster R-CNN, cascade R-CNN) Res-Net residual neural network, an artificial neural network having shortcuts / skip connections between different layers SOM self-organising maps, an algorithm for generating a low-dimensional representation of data while preserving the topological structure of the data SSD single shot (multibox) detector, a neural network for object detection U-Net neural network having a specific layer structure YOLO you only look once, an artificial neural network used for object detection (comes in various versions: YOLO v2, YOLO v3 etc.)