Machine Learning Glossary: Difference between revisions
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* Fréchet Inception Distance (FID) - a reference-based GAN evaluation metric which passes images through a pretrained network (typically Inception) and compares the distribution of intermediate features. | * Fréchet Inception Distance (FID) - a reference-based GAN evaluation metric which passes images through a pretrained network (typically Inception) and compares the distribution of intermediate features. | ||
* Fully connected network - The standard neural network model where each layer is a sequence of nodes. | * Fully connected network - The standard neural network model where each layer is a sequence of nodes. | ||
* Features | * Features - a generic term indicating the latent inputs or intermediate outputs of a neural network (2D = feature map, 3D = feature grid). | ||
==G== | ==G== | ||
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==N== | ==N== | ||
* Neurons - Individual elements in a MLP layer (perceptron + activation) which supposedly resemble brain neurons. | * Neurons - Individual elements in a MLP layer (perceptron + activation) which supposedly resemble brain neurons. | ||
* Neural Fields - A subfield of computer vision and graphics which uses neural networks to represent 3D scenes and perform tasks such as 3D reconstruction | * Neural Fields - A subfield of computer vision and graphics which uses neural networks to represent 2D/3D scenes and perform tasks such as 3D reconstruction, scene generation, and image compression. | ||
* Normalized Device Coordinates - In images, pixels are in coordinates of <math>[-1, 1]\times[-1, 1] </math>. | * Normalized Device Coordinates - In images, pixels are in coordinates of <math>[-1, 1]\times[-1, 1] </math>. | ||
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* Perceptron - a linear classifier. | * Perceptron - a linear classifier. | ||
* Perceptual loss - a loss function which passes images through a pretrained network (e.g. VGG) and compares intermediate features instead of raw pixels. | * Perceptual loss - a loss function which passes images through a pretrained network (e.g. VGG) and compares intermediate features instead of raw pixels. | ||
* Positional encoding - Applying sin/cos at various frequencies (i.e. fourier basis) so the network can distinguish input values at different scales. Used in | * Positional encoding - Applying sin/cos at various frequencies (i.e. fourier basis) so the network can distinguish input values at different scales. Used in neural fields as well as NLP models to encode the relative positions of inputs. | ||
==R== | ==R== |