Machine Learning Glossary: Difference between revisions

(2 intermediate revisions by the same user not shown)

Line 31:

* 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.

* Features ~~or Feature map~~ - a generic term indicating the intermediate outputs of a neural network

* Features - a generic term indicating the latent inputs or intermediate outputs of a neural network (2D = feature map, 3D = feature grid).

==G==

Line 65:

==N==

* 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 ~~and~~ scene generation.

* 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>.

Line 75:

* 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.

* Positional encoding - Applying sin/cos at various frequencies (i.e. fourier basis) so the network can distinguish input values at different scales. Used in ~~NeRF~~ as well as NLP models to encode the relative positions of inputs.

* 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==

@@ Line 31: / Line 31: @@
 * 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.
-* Features or Feature map - a generic term indicating the intermediate outputs of a neural network
+* Features - a generic term indicating the latent inputs or intermediate outputs of a neural network (2D = feature map, 3D = feature grid).
 ==G==
@@ Line 65: / Line 65: @@
 ==N==
 * 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 and scene generation.
+* 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>.
@@ Line 75: / Line 75: @@
 * 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.
-* Positional encoding - Applying sin/cos at various frequencies (i.e. fourier basis) so the network can distinguish input values at different scales. Used in NeRF as well as NLP models to encode the relative positions of inputs.
+* 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==