Adversarial Separation Network for Text Style Transfer

This article considers the task of text style transfer: transforming a specific style of sentence into another while preserving its style-independent content. A dominate approach to text style transfer is to learn a good content factor of text, define a fixed vector for every style and recombine them to generate text in the required style. In fact, there are a large number of different words to convey the same style from different aspects. Thus, using a fixed vector to represent one style is very inefficient, which causes the weak representation power of the style vector and limits text diversity of the same style. To address this problem, we propose a novel neural generative model called Adversarial Separation Network (ASN), which can learn the content and style vector jointly and the learnt vectors have strong representation power and good interpretabilities. In our method, adversarial learning is implemented to enhance our model’s capability of disentangling the two factors. To evaluate our method, we conduct experiments on two benchmark datasets. Experimental results show our method can perform style transfer better than strong comparison systems. We also demonstrate the strong interpretability of the learnt latent vectors.

A deep generative model for multi-view profiling of single-cell RNA-seq and ATAC-seq data

Here, we present a multi-modal deep generative model, the single-cell Multi-View Profiler (scMVP), which is designed for handling sequencing data that simultaneously measure gene expression and chromatin accessibility in the same cell, including SNARE-seq, sci-CAR, Paired-seq, SHARE-seq, and Multiome from 10X Genomics. scMVP generates common latent representations for dimensionality reduction, cell clustering, and developmental trajectory inference and generates separate imputations for differential analysis and cis-regulatory element identification. scMVP can help mitigate data sparsity issues with imputation and accurately identify cell groups for different joint profiling techniques with common latent embedding, and we demonstrate its advantages on several realistic datasets.

AOGAN: A generative adversarial network for screen space ambient occlusion

Ambient occlusion (AO) is a widely-used real-time rendering technique which estimates light intensity on visible scene surfaces. Recently, a number of learning-based AO approaches have been proposed, which bring a new angle to solving screen space shading via a unified learning framework with competitive quality and speed. However, most such methods have high error for complex scenes or tend to ignore details. We propose an end-to-end generative adversarial network for the production of realistic AO, and explore the importance of perceptual loss in the generative model to AO accuracy. An attention mechanism is also described to improve the accuracy of details, whose effectiveness is demonstrated on a wide variety of scenes.