AWS Certified AI PractitionerAI/ML and Generative AI FundamentalsMedium
A developer is building a generative AI model to create realistic images of animals. After initial training, the model produces images that are blurry and lack fine details. Which of the following generative model architectures is best suited to address this issue by improving the sharpness and quality of generated images?
- AGenerative Adversarial Network (GAN)
- BSupport Vector Machine (SVM)
- CRecurrent Neural Network (RNN)
- DDecision Tree
Show answer & explanationAnswer & explanation
Correct answer: A. Generative Adversarial Network (GAN)
Generative Adversarial Networks (GANs) are specifically designed for generating realistic and high-quality data, such as images. They consist of a generator and a discriminator network that compete, leading to the generator producing increasingly realistic outputs that can fool the discriminator, thus improving sharpness and detail.
Why the other options are wrong
- B. SVMs are supervised learning models used for classification and regression, not for generative tasks.
- C. RNNs are primarily used for sequential data like time series or natural language, not typically for image generation quality.
- D. Decision Trees are used for classification and regression, not for generating complex data like images.
Generative Adversarial Networks (GANs)
A class of generative AI models consisting of two neural networks, a generator and a discriminator, that compete against each other to produce new, realistic data.
- Generator creates synthetic data.
- Discriminator distinguishes real from synthetic data.
- Used for image generation, style transfer, and data augmentation.
Memory trick: Generative models 'create' new things, often through 'adversarial' competition.