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Generative Adversarial Networks (GANs)

Last updated Nov 14, 2024 Edit Source

A GAN consists of two networks; a generator (G) and a discriminator (D), given a set of training examples, G will generate outputs and D will classify them as either being from the same distribution as the training examples or not. In doing so D is optimized so as to be able to discriminate between examples from the training example and from the generator network which in turn is optimized to fool D into classifying its output as being drawn from the training examples. After such training G can now generate samples with properties very similar to those of the training examples. GANs tend to be devilishly hard to train

See AI/Generative AI/GenAI

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# References

# Subtopics

# GANs for super-resolution

See AI/Computer Vision/Super-resolution

# GANs for missing data, imputation and inpainting

See AI/Computer Vision/Inpainting and restoration

# Image-to-image translation. Conditional GANs

See AI/Computer Vision/Image-to-image translation

# GANs for spatio-temporal data generation

# GANs for representation learning and image synthesis

# Semi-supervised GANs

# Few/one-shot learning GANs

See AI/One, few-shot learning

# GANs for anomaly detection