Diffusion models, Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs) are all methods used for generating new data samples, but they operate on different principles. A diffusion model generates data by reversing a gradual noising process, where it starts with pure noise and iteratively refines it to produce a coherent sample. GANs, in contrast, consist of two neural networks: a generator that creates new data and a discriminator that evaluates how realistic the generated data is. VAEs incorporate a probabilistic approach, mapping input data to a latent space from which new samples can be drawn, making them effective for tasks involving uncertainty and representation learning.
One key aspect of diffusion models is their stability during training. Unlike GANs, which can suffer from mode collapse—where the generator fails to produce diverse outputs or matches only a few data distributions—diffusion models tend to exhibit more consistent results across a wider range of inputs. For example, a diffusion model trained on images will likely generate not only a single style of images but a broader variety of similar images, as it refines noise into data through many iterative steps. This iterative refinement often results in high-quality outputs that capture details better than those generated by GANs, especially in high-dimensional data like images.
VAEs differ from both diffusion models and GANs in that they focus on capturing the underlying distribution of the data rather than explicitly generating new samples. VAEs achieve this by learning to encode input data into a lower-dimensional latent space and then decoding it back into the original space. This makes them particularly good for tasks requiring interpolation between data points. For instance, VAEs can be useful in applications like data compression or anomaly detection. While diffusion models and GANs might excel in high fidelity image generation, VAEs provide valuable insights into the structure of the data, which can be beneficial in contexts where understanding the data is as important as generating new samples.