MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generation

Recently, generative adversarial networks (GANs) have shown promising performance in generating realistic images. However, they often struggle in learning complex underlying modalities in a given dataset, resulting in poor-quality generated images. To mitigate this problem, we present a novel approach called mixture of experts GAN (MEGAN), an ensemble approach of multiple generator networks. Each generator network in MEGAN specializes in generating images with a particular subset of modalities, e.g., an image class. Instead of incorporating a separate step of handcrafted clustering of multiple modalities, our proposed model is trained through an end-to-end learning of multiple generators via gating networks, which is responsible for choosing the appropriate generator network for a given condition. We adopt the categorical reparameterization trick for a categorical decision to be made in selecting a generator while maintaining the flow of the gradients. We demonstrate that individual generators learn different and salient subparts of the data and achieve a multiscale structural similarity (MS-SSIM) score of 0.2470 for CelebA and a competitive unsupervised inception score of 8.33 in CIFAR-10.Comment: 27th International Joint Conference on Artificial Intelligence (IJCAI 2018

Production of Metal-Free C, N Alternating Nanoplatelets and Their In Vivo Fluorescence Imaging Performance without Labeling

The use of luminescent probes with proper optical and morphological properties, high serum stability, low cytotoxicity, and good biocompatibility is a cost-effective method for bioimaging. In this work, a route is developed to produce a novel bioimaging probe framework. A C(3)N(4)material (UCN-H) is produced by thermal condensation of urea under humidified air treatment. Chemical characterizations reveal that the UCN-H contains C(3)N(4)networks with smaller grain sizes and more amine-based functionalities at the edges than UCN, which is separately produced without the humidified air treatment. Highly stable aqueous dispersions including fluorescent C(3)N(4)nanoplatelets are generated by sonication of the UCN-H powder. The photoluminescence (PL), time resolved-PL, and 2D excitation-emission spectra of the dispersions show that the UCN-H has less-intra bandgap traps and longer PL lifetime than UCN. In confocal microscopic study using the nanoplatelets, clear fluorescent cell images are obtained without any cytosolic aggregation. In in vivo imaging studies with MDA-MB-231 tumor-bearing mice models, persistently strong fluorescence signals are successfully observed on tumor lesions without any interference of autofluorescence from live tissues after their accumulation by passive tumor targeting. Ex vivo biodistribution and histology results are well-matched with in vivo fluorescence imaging results.