Models, code, and papers for "Chunyu Fang":

High-throughput, high-resolution registration-free generated adversarial network microscopy

Oct 03, 2018
Hao Zhang, Xinlin Xie, Chunyu Fang, Yicong Yang, Di Jin, Peng Fei

We combine generative adversarial network (GAN) with light microscopy to achieve deep learning super-resolution under a large field of view (FOV). By appropriately adopting prior microscopy data in an adversarial training, the neural network can recover a high-resolution, accurate image of new specimen from its single low-resolution measurement. Its capacity has been broadly demonstrated via imaging various types of samples, such as USAF resolution target, human pathological slides, fluorescence-labelled fibroblast cells, and deep tissues in transgenic mouse brain, by both wide-field and light-sheet microscopes. The gigapixel, multi-color reconstruction of these samples verifies a successful GAN-based single image super-resolution procedure. We also propose an image degrading model to generate low resolution images for training, making our approach free from the complex image registration during training dataset preparation. After a welltrained network being created, this deep learning-based imaging approach is capable of recovering a large FOV (~95 mm2), high-resolution (~1.7 {\mu}m) image at high speed (within 1 second), while not necessarily introducing any changes to the setup of existing microscopes.

* 21 pages, 9 figures and 1 table. Peng Fe and Di Jin conceived the ides, initiated the investigation. Hao Zhang, Di Jin and Peng Fei prepared the manuscript 

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Implicit Deep Latent Variable Models for Text Generation

Sep 18, 2019
Le Fang, Chunyuan Li, Jianfeng Gao, Wen Dong, Changyou Chen

Deep latent variable models (LVM) such as variational auto-encoder (VAE) have recently played an important role in text generation. One key factor is the exploitation of smooth latent structures to guide the generation. However, the representation power of VAEs is limited due to two reasons: (1) the Gaussian assumption is often made on the variational posteriors; and meanwhile (2) a notorious "posterior collapse" issue occurs. In this paper, we advocate sample-based representations of variational distributions for natural language, leading to implicit latent features, which can provide flexible representation power compared with Gaussian-based posteriors. We further develop an LVM to directly match the aggregated posterior to the prior. It can be viewed as a natural extension of VAEs with a regularization of maximizing mutual information, mitigating the "posterior collapse" issue. We demonstrate the effectiveness and versatility of our models in various text generation scenarios, including language modeling, unaligned style transfer, and dialog response generation. The source code to reproduce our experimental results is available on GitHub.

* 13 pages, 8 Tables, 1 Figure, Accepted at 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP 2019) 

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