Conditional GAN
A generative adversarial network whose generation and discrimination are conditioned on auxiliary information, such as class labels, to guide the produced outputs.
Learn Generative AI with PyTorch
A GAN that conditions generation on auxiliary information such as class labels so the output can be steered toward a desired attribute.
- How it is used
- Directs synthetic image generation toward labeled attributes such as eyeglasses, gender-related facial features, or other selected characteristics.
- How deep it goes
- Explains conditioning, tensor construction, network roles, training batches, visual inspection, and WGAN stabilization without deriving the full theory.
- Examples
- Uses labeled human faces with or without eyeglasses and demonstrates one-hot labels, generated-image grids, and attribute transitions.
- What you can do after
- Build it from scratch in a convolutional PyTorch model, train it with labeled batches, inspect outputs, and generate a chosen attribute.
Read how Learn Generative AI with PyTorch explains conditional GAN →
Inside Deep Learning: Math, Algorithms, Models
A GAN variant that conditions generation on additional information such as class labels.
- How it is used
- Uses class labels to steer GAN generation toward requested categories and reduce the generator’s need to infer class structure.
- How deep it goes
- Explains the one-to-many mapping, wrapper architecture, training-call changes, and shared-latent sampling, while deferring deeper conditioning methods.
- Examples
- Demonstrates ten-class digit generation, shared latent vectors, preserved handwriting style, and PyTorch ConditionalWrapper listings.
- What you can do after
- Build it from scratch by wrapping PyTorch generator and discriminator networks, training with labels, and sampling selected classes.
Read how Inside Deep Learning: Math, Algorithms, Models explains conditional GAN →
Related concepts
- labeluses · A label is a value or annotation associated with an item that identifies its target category, outcome, or reference answer for learning or evaluation.
- latent spaceuses · A latent space is a learned representational space in which numerical coordinates encode information or features inferred from observed data.
- generative adversarial networkkind of · A generative adversarial network is a generative model with a generator and discriminator trained in competition, enabling the generator to produce samples resembling a target dataset or distribution.
- Wasserstein distanceuses · Wasserstein distance is a distance between probability distributions defined by the minimum cost of transporting probability mass between them under a specified ground distance.
- categorical encodinguses · Categorical encoding transforms discrete category values into numerical representations, such as separate binary indicators or vectors with one active position per category.
- conditional probabilityuses · Conditional probability is the probability of an event or variable value given that specified information, an event, or another variable’s value is known.
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