Instance Generation

Instance generation focuses on creating synthetic data instances, addressing the limitations of relying solely on real-world datasets. Current research emphasizes generating multiple instances simultaneously with precise control over attributes like position, shape, and color, often leveraging diffusion models, generative adversarial networks (GANs), and attention mechanisms within these architectures. This work is crucial for improving the performance of machine learning models across various domains, particularly where real data is scarce or expensive to acquire, and for enabling more robust and controlled experimentation. The development of benchmark datasets and instance generators is also a significant area of focus, facilitating fair comparison and advancement of the field.