Modular GANs can be implemented to generate multi-object images with varying levels of complexity by dividing the model architecture into separate modules, each responsible for developing different parts of the image or handling different objects.
You can follow the following steps in order to implement:
- Modular Architecture: Create separate generator modules for different components of the image (e.g., background, foreground, and objects).
- Conditioning: Use conditional inputs (e.g., object types, locations) to guide each module in generating specific parts of the image.
- Combining Outputs: Combine the outputs from each module to form a cohesive image.
- Discriminators: Multiple discriminators are used, each responsible for evaluating different aspects of the generated image (e.g., object realism, background consistency).
Here is the code snippet showing the above steps:
In the above code, we are using the following key features:
- Modular Architecture: The generator is divided into modules for background and object generation, allowing for targeted generation of different parts of the image.
- Independent Control: Each module can be controlled independently by varying the input conditions (e.g., types of objects and their positions).
- Image Composition: The outputs from each generator module (background and objects) are combined to form a coherent image.
- Discriminator: The discriminator evaluates the realism of the entire image, encouraging both the background and objects to appear natural and consistent.
Hence, by referring to the above, you can implement Modular GANs to generate multi-object images with varying levels of complexity.
