Machine Learning Glossary

Machine Learning, Computer Vision, and Computer Graphics Glossary

C

• Capsule neural network
• Convolutional neural network or CNN - A neural network architecture for image data, or other data on a regular grid.

D

• Dilation - how spread out a CNN kernel is. See Convolutional neural network.
• Domain Adaptation - An area of research focused on making neural network work with alternate domains, or sources of data.

E

• Early stopping - a technique where you stop training once the validation loss begins increasing. This is not used very often anymore with large models.

F

• Fully connected network - The standard neural network model where each layer is a sequence of nodes.

G

• Generative adversarial network or GAN - A neural network setup for generating examples from a training distribution.
• Graph Neural Network

I

• Intersection over Union - A metric for computing the accuracy of bounding box prediction.

L

• Long short-term memory or LSTM - An RNN neural network architecture which has two sets of hidden states for long and short term.

M

• Multilayer perceptron (MLP) - See Fully connected network.

N

• Normalized Device Coordinates - In images, pixels are in coordinates of \(\displaystyle [-1, 1]\times[-1, 1] \).

O

• Overfitting - when a model begins to learn noise specific to your training data, thereby worsening performance on non-training data.

R

• Recurrent neural network (RNN) - A type of neural network which operates sequentially on sequence data.
• Reinforcement Learning - an area of machine learning focused on learning to perform actions, E.g. playing a game

S

• Stride - how far the CNN kernel in terms of input pixels moves between output pixels.

T

• Transfer Learning - Techniques to make a neural network perform a different task than what it is trained on.
• Transformer (machine learning model) - A neural network architecture for sequence data which uses attention between elements of the sequence.

U

• Underfitting - when a model performs poorly on both training and validation data, usually due to inadequate model complexity or training duration.