Generative AI Tutorials

Generative AI - Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs)

Introduced by Ian Goodfellow and his team, in 2014 Generative Adversarial Networks, or GANs consist of two networks; the generator and the discriminator. In a setup, the generator aims to produce data resembling real data while the discriminator works to differentiate between authentic and fabricated data. This adversarial cycle persists until the generator generates data from data to the discriminator.

Structure and Functioning

There are two basic parts to the structure of GANs:

  • Generator: This network uses random noise as input and converts it into data that looks like the genuine dataset in order to create false data.
  • Discriminator: This network compares the generated data to actual data and gives the generator feedback.

The discriminator evaluates the fictitious data that the generator creates as part of the training process. With time, the generator gains more realistic data generation skills by assimilating discriminator feedback. The back-and-forth mechanism behind GANs' remarkable strength and adaptability.

Applications and Examples

GANs are used for many different tasks, such as data augmentation and picture synthesis. Among the noteworthy instances are:

  • Image Generation: For a variety of uses, including entertainment, design, and the arts, GANs are used to produce realistic pictures.
  • Data Augmentation: GANs can provide more training data for machine learning models, which is especially helpful when there is a lack of available data.
  • Super-Resolution: To increase an image's resolution and hence its quality and detail, GANs are used.

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Generative AI

Beginner 5 Hours

Generative Adversarial Networks (GANs)

Introduced by Ian Goodfellow and his team, in 2014 Generative Adversarial Networks, or GANs consist of two networks; the generator and the discriminator. In a setup, the generator aims to produce data resembling real data while the discriminator works to differentiate between authentic and fabricated data. This adversarial cycle persists until the generator generates data from data to the discriminator.

Structure and Functioning

There are two basic parts to the structure of GANs:

  • Generator: This network uses random noise as input and converts it into data that looks like the genuine dataset in order to create false data.
  • Discriminator: This network compares the generated data to actual data and gives the generator feedback.

The discriminator evaluates the fictitious data that the generator creates as part of the training process. With time, the generator gains more realistic data generation skills by assimilating discriminator feedback. The back-and-forth mechanism behind GANs' remarkable strength and adaptability.

Applications and Examples

GANs are used for many different tasks, such as data augmentation and picture synthesis. Among the noteworthy instances are:

  • Image Generation: For a variety of uses, including entertainment, design, and the arts, GANs are used to produce realistic pictures.
  • Data Augmentation: GANs can provide more training data for machine learning models, which is especially helpful when there is a lack of available data.
  • Super-Resolution: To increase an image's resolution and hence its quality and detail, GANs are used.

Related Tutorials

Frequently Asked Questions for Generative AI

Sequence of prompts stored as linked records or documents.

It helps with filtering, categorization, and evaluating generated outputs.



As text fields, often with associated metadata and response outputs.

Combines keyword and vector-based search for improved result relevance.

Yes, for storing structured prompt-response pairs or evaluation data.

Combines database search with generation to improve accuracy and grounding.

Using encryption, anonymization, and role-based access control.

Using tools like DVC or MLflow with database or cloud storage.

Databases optimized to store and search high-dimensional embeddings efficiently.

They enable semantic search and similarity-based retrieval for better context.

They provide organized and labeled datasets for supervised trainining.



Track usage patterns, feedback, and model behavior over time.

Enhancing model responses by referencing external, trustworthy data sources.

They store training data and generated outputs for model development and evaluation.

Removing repeated data to reduce bias and improve model generalization.

Yes, using BLOB fields or linking to external model repositories.

With user IDs, timestamps, and quality scores in relational or NoSQL databases.

Using distributed databases, replication, and sharding.

NoSQL or vector databases like Pinecone, Weaviate, or Elasticsearch.

With indexing, metadata tagging, and structured formats for efficient access.

Text, images, audio, and structured data from diverse databases.

Yes, for representing relationships between entities in generated content.

Yes, using structured or document databases with timestamps and session data.

They store synthetic data alongside real data with clear metadata separation.



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