English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deep generative models (DGMs) are data-eager because learning a complex modelon limited data suffers from a large variance and easily overfits. Inspired bythe classical perspective of the bias-variance tradeoff, we propose regularizeddeep generative model (Reg-DGM), which leverages a nontransferable pre-trainedmodel to reduce the variance of generative modeling with limited data.Formally, Reg-DGM optimizes a weighted sum of a certain divergence and theexpectation of an energy function, where the divergence is between the data andthe model distributions, and the energy function is defined by the pre-trainedmodel w.r.t. the model distribution. We analyze a simple yet representativeGaussian-fitting case to demonstrate how the weighting hyperparameter tradesoff the bias and the variance. Theoretically, we characterize the existence andthe uniqueness of the global minimum of Reg-DGM in a non-parametric setting andprove its convergence with neural networks trained by gradient-based methods.Empirically, with various pre-trained feature extractors and a data-dependentenergy function, Reg-DGM consistently improves the generation performance ofstrong DGMs with limited data and achieves competitive results to thestate-of-the-art methods. Our implementation is available athttps://github.com/ML-GSAI/Reg-ADA-APA.

Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models