Who does this allow for the creation of synthetic data?
Latent space activation enables the creation of synthetic data through several mechanisms. Here's a brief explanation:
Learned representations: Models capture essential features of data in the latent space.
Interpolation: By activating different points in this space, we can generate new, unseen examples.
Controlled generation: Manipulating specific dimensions in the latent space allows for targeted attribute changes.
Sampling: Drawing random points from the latent distribution creates diverse synthetic samples.
Feature disentanglement: Well-structured latent spaces separate different data attributes, enabling fine-grained control.
This process is particularly powerful in generative models like GANs and VAEs.