Results for "noise schedule"

Controls amount of noise added at each diffusion step.

When a model fits noise/idiosyncrasies of training data and performs poorly on unseen data.

Expanding training data via transformations (flips, noise, paraphrases) to improve robustness.

Measures a model’s ability to fit random noise; used to bound generalization error.

Generative model that learns to reverse a gradual noise process.

Diffusion model trained to remove noise step by step.

Adjusting learning rate over training to improve convergence.

Variability introduced by minibatch sampling during SGD.