Search Results for author:
Found 2 papers, 1 papers with code
Reducing Training Sample Memorization in GANs by Training with Memorization Rejection
In this paper, we propose memorization rejection, a training scheme that rejects generated samples that are near-duplicates of training samples during training.
A Large-scale Study on Training Sample Memorization in Generative Modeling
Many recent developments on generative models for natural images have relied on heuristically-motivated metrics that can be easily gamed by memorizing a small sample from the true distribution or training a model directly to improve the metric.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
