Demystifying Diffusion Models
Generative models learns a representation of the data they are trained on and allows you to synthesize novel data , different from the real data , but one that looks realistic and also maintains distribution. Generative models have become a prevalent approach for tasks related to image processing and synthesis, including but not limited to editing, in-painting, de-blurring, colorization, and super resolution.
The data representations learned by the neural networks of generative models, as well as the synthesized data, can often be used for training and fine-tuning other downstream machine learning models for different tasks, especially when labeled data is scarce.
In order to adopt generative models in real-world applications, it is essential for these models to satisfy some key requirements —
- Quality
Generating high-quality outputs is crucial for many applications that involve user interaction as well as for the applications that use data for downstream tasks. If the synthetic data contains artifacts or inconsistencies, it may introduce bias or noise into the training data and lead to inaccurate model predictions. for eg, in medical imaging, high quality data is essential for accurate diagnosis and treatment of patients. Any errors or artifacts in the images can lead to incorrect diagnoses or ineffective treatments, which can have serious…