The Vortex members are effortlessly charming, and Kit is very much a typical fangirl. The first few chapters are mostly lighthearted and fun. At this point, you may think you already know where this story is going (I thought I did too), but here is where I will make it clear: "straightforward" is the LAST word I would use to describe this book. While the plot is heavily focused on the boys, Trick of the Spotlight is told from the perspective of Kit Allister, an American college drop-out who has forged her own path to K-pop stardom through her undying love for Vortex. And I have to say, from the sassy, scandalous Kim Mino to the regal, no-nonsense Mahn Jaeyoon, I just may have let out a few fangirl squeals thanks to their hilarious antics and genuine dynamic. The boys are introduced right off the bat in the first chapter, and while it did take me a bit of time to familiarize myself with such a large cast, they do manage to distinguish themselves fairly quickly with their vastly different personalities. It features a five-member boy band known as Vortex, hailed as perhaps the greatest international success the pop music industry has ever seen. On the surface level, Trick of the Spotlight certainly does appear to be a straightforward boy-meets-girl romance novel. I was definitely skeptical going into it, but I'm SO glad I decided to trust her opinion on this one. This book was recommended to me by a friend who is well aware that I hate pretty much everything having to do with the romance genre.
To the best of our knowledge, NVAE is the first successful VAEĪpplied to natural images as large as 256$\times$256 pixels.If you're thinking about reading this book, DO IT! For example, on CIFAR-10, NVAE pushes the state-of-the-artįrom 2.98 to 2.91 bits per dimension, and it produces high-quality images onĬelebA HQ. The MNIST, CIFAR-10, CelebA 64, and CelebA HQ datasets and it provides a strongīaseline on FFHQ. State-of-the-art results among non-autoregressive likelihood-based models on Residual parameterization of Normal distributions and its training is Separable convolutions and batch normalization. (NVAE), a deep hierarchical VAE built for image generation using depth-wise The statistical challenges, we explore the orthogonal direction of carefullyĭesigning neural architectures for hierarchical VAEs. While the majority of the research in VAEs is focused on However, they areĬurrently outperformed by other models such as normalizing flows andĪutoregressive models. Tractable sampling and easy-to-access encoding networks. Among them, VAEs have the advantage of fast and Download a PDF of the paper titled NVAE: A Deep Hierarchical Variational Autoencoder, by Arash Vahdat and 1 other authors Download PDF Abstract: Normalizing flows, autoregressive models, variational autoencoders (VAEs),Īnd deep energy-based models are among competing likelihood-based frameworksįor deep generative learning.
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