In the context of image generation, what does augmentation involve?

Augmentation in the context of image generation refers to manipulating images to create variations. This process enhances the diversity of the training data without the need to collect new data. Techniques such as rotating, flipping, cropping, or adjusting the brightness of images are common forms of augmentation. These variations help improve the performance of generative models by exposing them to a broader range of input scenarios, making them more robust to different conditions during inference. By augmenting images, models can learn to generalize better, thereby improving their accuracy when generating new images or recognizing patterns in existing ones.