  Generative models have proven to be an outstanding tool for representing
high-dimensional probability distributions and generating realistic-looking
images. An essential characteristic of generative models is their ability to
produce multi-modal outputs. However, while training, they are often
susceptible to mode collapse, that is models are limited in mapping input noise
to only a few modes of the true data distribution. In this work, we draw
inspiration from Determinantal Point Process (DPP) to propose an unsupervised
penalty loss that alleviates mode collapse while producing higher quality
samples. DPP is an elegant probabilistic measure used to model negative
correlations within a subset and hence quantify its diversity. We use DPP
kernel to model the diversity in real data as well as in synthetic data. Then,
we devise an objective term that encourages generators to synthesize data with
similar diversity to real data. In contrast to previous state-of-the-art
generative models that tend to use additional trainable parameters or complex
training paradigms, our method does not change the original training scheme.
Embedded in an adversarial training and variational autoencoder, our Generative
DPP approach shows a consistent resistance to mode-collapse on a wide variety
of synthetic data and natural image datasets including MNIST, CIFAR10, and
CelebA, while outperforming state-of-the-art methods for data-efficiency,
generation quality, and convergence-time whereas being 5.8x faster than its
closest competitor.
