GAN
A Generative Adversarial Network (GAN) is a machine learning framework, introduced by Ian Goodfellow and colleagues in 2014, in which two neural networks — a generator and a discriminator — are trained together in competition, with the generator learning to produce increasingly realistic synthetic data (such as…
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Glossary Terms(6)
YOLO
YOLO (You Only Look Once) is a family of real-time object detection models, originally introduced by Joseph Redmon in 2016, that detect and classify multiple o…
ResNet
ResNet (Residual Network) is a convolutional neural network architecture introduced by Microsoft Research in 2015 that uses "skip connections" to let very deep…
GAN
A Generative Adversarial Network (GAN) is a machine learning framework, introduced by Ian Goodfellow and colleagues in 2014, in which two neural networks — a g…
Diffusion Model
A diffusion model is a class of generative machine learning model that creates new data — most commonly images — by learning to reverse a gradual noising proce…
Variational Autoencoder (VAE)
A Variational Autoencoder (VAE) is a generative neural network architecture, introduced by Diederik Kingma and Max Welling in 2013, that learns to compress dat…
Synthetic Data
Synthetic data is artificially generated data — produced by algorithms, simulations, or generative models rather than collected from real-world events — that i…