A deep learning model consisting of two networks, a generator and a discriminator, that compete to improve image generation.

The component of a GAN that creates new images from random noise, aiming to produce outputs indistinguishable from real images.

The part of a GAN that evaluates images, distinguishing between real and generated images to guide the generator's learning.

A dataset used to train the GAN, consisting of real images that the generator learns to replicate.

Settings that govern the learning process of a GAN, such as learning rate and batch size, crucial for performance optimization.

A metric used to evaluate the quality of generated images by comparing their distribution to that of real images.

A metric that assesses the quality and diversity of generated images based on a pre-trained neural network's predictions.

A modeling error where the GAN learns noise in the training data, reducing its ability to generalize to new data.

The point at which the GAN's training stabilizes, resulting in consistent output quality and diversity.

A single pass through the entire training dataset during the GAN's training process.

Random input fed into the generator to create diverse outputs; essential for generating unique images.

Techniques used to artificially expand the training dataset by modifying existing images, enhancing model robustness.

A method where a pre-trained model is fine-tuned on a new dataset, often used to speed up GAN training.

A subjective measure of how realistic or visually appealing generated images are, evaluated through various metrics.

The variety of outputs generated by a GAN, indicating its ability to create different images from similar inputs.

The study of moral implications and responsibilities in developing and deploying AI technologies, including GANs.

Legal rights concerning the use and distribution of original works, relevant to AI-generated content.

The degree to which an image can be considered genuine or original, particularly in the context of AI-generated art.

Sectors that rely on creativity for economic value, including art, design, and entertainment, where GANs are applied.

Uses of GANs in various fields, such as art generation, video game graphics, and realistic simulations.

Standards used to assess the performance of GANs, including FID, IS, and user studies.

Assessment based on personal opinions and experiences, often used to gauge the quality of AI-generated content.

The extent to which generated images resemble real-world images, crucial for applications in creative fields.

Software libraries like TensorFlow and PyTorch used to build and train GANs efficiently.