Generative AI Glossary
Prompt
Create a glossary of terms for a class on generative AI.
Some terms will have an abbreviation.
Return the results in a single raw markdown file.
For each term use the following format:
#### Term (Abbreviation)
Defintion
Adversarial Attack
An attempt to deceive a machine learning model through malicious input. This can be done to test the robustness of the model or for malicious intent.
Adversarial Training
A training methodology where a model is trained to defend against adversarial attacks by including adversarial examples in its training data.
Autoencoder (AE)
A neural network used for unsupervised learning of efficient encodings by training the network to output its input, generally through a bottleneck layer to produce a compressed representation.
Backpropagation (Backprop)
A widely used algorithm in training feedforward neural networks for supervised learning. It computes the gradient of the loss function with respect to each weight by the chain rule of calculus.
Conditional Generative Adversarial Network (cGAN)
A type of GAN that generates data based on certain conditions or labels, allowing more control over the data it produces.
COSTAR Framework
An abbreviation for six things to consider when creating a prompt.
- Context
- Objective
- Style
- Tone
- Audience
-
Response
Data Augmentation
Techniques used to increase the amount of training data by applying various transformations on the existing data, such as rotations, translations, and scaling.
Deepfake
Hyper-realistic but entirely fake content generated using deep learning techniques. Often refers to videos where a person's likeness is swapped with another's.
Discriminator
In the context of GANs, it's the component that distinguishes between real and generated samples. Its objective is to classify real samples correctly and to reject generated samples.
Encoder
The part of an autoencoder that compresses the input into a compact representation.
Decoder
The part of an autoencoder that reconstructs the input from the compact representation.
Few-Shot Learning
Description: A machine learning approach where a model is trained to recognize new tasks or categories with very limited data, typically a handful of examples.
Generative Adversarial Network (GAN)
A class of machine learning frameworks where two networks, the generator and the discriminator, are trained simultaneously through adversarial processes.
Generator
In the context of GANs, it's the component that tries to generate data. Its objective is to produce data that the discriminator cannot distinguish from real data.
Latent Space
A compressed, abstract representation of data where similar data points are close together. It's often used in generative models as the space from which they sample to generate new data.
Loss Function
A function that measures the difference between the predicted output and the actual output. It's used to update model weights during training.
Neural Style Transfer
A technique in deep learning that applies the style of one image to transform the content of another image, while preserving the content's structure and detail.
One-Shot Learning
A machine learning paradigm where a model is trained to recognize patterns or make decisions based on only a single example or very few examples of each class or category.
Reinforcement Learning (RL)
A type of machine learning where an agent learns to behave in an environment by performing certain actions and receiving rewards or penalties in return.
Transfer Learning
A machine learning method where a model developed for a task is reused as the starting point for a model on a second task.
Variational Autoencoder (VAE)
A type of autoencoder that aims to learn a probabilistic mapping between the data space and the latent space. It's used for generating new data that's similar to the input data.
Zero-Shot Learning
A type of machine learning where a model is trained to handle tasks for which it has seen no examples during training.