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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this paper, we present a novel approach for training a VariationalAutoencoder (VAE) on a highly imbalanced data set. The proposed training of ahigh-resolution VAE model begins with the training of a low-resolution coremodel, which can be successfully trained on imbalanced data set. In subsequenttraining steps, new convolutional, upsampling, deconvolutional, anddownsampling layers are iteratively attached to the model. In each iteration,the additional layers are trained based on the intermediate pretrained model -a result of previous training iterations. Thus, the resolution of the model isprogressively increased up to the required resolution level. In this paper, theprogressive VAE training is exploited for learning a latent representation withimbalanced, highly sparse data sets and, consequently, generating routes in aconstrained 2D space. Routing problems (e.g., vehicle routing problem,travelling salesman problem, and arc routing) are of special significance inmany modern applications (e.g., route planning, network maintenance, developinghigh-performance nanoelectronic systems, and others) and typically associatedwith sparse imbalanced data. In this paper, the critical problem of routingbillions of components in nanoelectronic devices is considered. The proposedapproach exhibits a significant training speedup as compared withstate-of-the-art existing VAE training methods, while generating expected imageoutputs from unseen input data. Furthermore, the final progressive VAE modelsexhibit much more precise output representation, than the GenerativeAdversarial Network (GAN) models trained with comparable training time. Theproposed method is expected to be applicable to a wide range of applications,including but not limited image impainting, sentence interpolation, andsemi-supervised learning.

Progressive VAE Training on Highly Sparse and Imbalanced Data