Computer poetry generation is our first step towards computer writing.
Writing must have a theme. The current approaches of using sequence-to-sequence
models with attention often produce non-thematic poems. We present a novel
conditional variational autoencoder with a hybrid decoder adding the
deconvolutional neural networks to the general recurrent neural networks to
fully learn topic information via latent variables. This approach significantly
improves the relevance of the generated poems by representing each line of the
poem not only in a context-sensitive manner but also in a holistic way that is
highly related to the given keyword and the learned topic. A proposed augmented
word2vec model further improves the rhythm and symmetry. Tests show that the
generated poems by our approach are mostly satisfying with regulated rules and
consistent themes, and 73.42% of them receive an Overall score no less than 3
(the highest score is 5).

本研究提出了一种新型的条件变量自编码器，该自编码器通过加入反卷积神经网络来完全学习潜在变量中的主题信息，进一步利用增强的 word2vec 模型来提高韵律和对称性，从而显著提高了生成诗歌的相关性，让生成的诗歌具有一致主题和规则性。测试结果表明，生成的诗歌在大多数情况下都得到了很好的评价。

使用混合解码器的条件变分自编码器生成主题中国诗歌

Generating Thematic Chinese Poetry using Conditional Variational  Autoencoders with Hybrid Decoders

Scene parsing is an important and challenging prob- lem in computer vision.
It requires labeling each pixel in an image with the category it belongs to.
Tradition- ally, it has been approached with hand-engineered features from
color information in images. Recently convolutional neural networks (CNNs),
which automatically learn hierar- chies of features, have achieved record
performance on the task. These approaches typically include a post-processing
technique, such as superpixels, to produce the final label- ing. In this paper,
we propose a novel network architecture that combines deep deconvolutional
neural networks with CNNs. Our experiments show that deconvolutional neu- ral
networks are capable of learning higher order image structure beyond edge
primitives in comparison to CNNs. The new network architecture is employed for
multi-patch training, introduced as part of this work. Multi-patch train- ing
makes it possible to effectively learn spatial priors from scenes. The proposed
approach yields state-of-the-art per- formance on four scene parsing datasets,
namely Stanford Background, SIFT Flow, CamVid, and KITTI. In addition, our
system has the added advantage of having a training system that can be
completely automated end-to-end with- out requiring any post-processing.

本研究提出了一种结合深度反卷积神经网络和卷积神经网络的新型神经网络，以实现场景解析。与卷积神经网络相比，反卷积神经网络在学习高阶图像结构方面表现更好。多补丁训练可以从场景中有效地学习空间先验知识。本方法在四个场景解析数据集上均取得了最先进的性能，并具有完全自动化的训练系统，无需后处理。

场景分割的深度反卷积网络

Deep Deconvolutional Networks for Scene Parsing

In sparse coding it is common to tile an image into nonoverlapping patches,
and then use a dictionary to create a sparse representation of each tile
independently. In this situation, the overcompleteness of the dictionary is the
number of dictionary elements divided by the patch size. In deconvolutional
neural networks (DCNs), dictionaries learned on nonoverlapping tiles are
replaced by a family of convolution kernels. Hence adjacent points in the
feature maps (V1 layers) have receptive fields in the image that are
translations of each other. The translational distance is determined by the
dimensions of V1 in comparison to the dimensions of the image space. We refer
to this translational distance as the stride.
We implement a type of DCN using a modified Locally Competitive Algorithm
(LCA) to investigate the relationship between the number of kernels, the
stride, the receptive field size, and the quality of reconstruction. We find,
for example, that for 16x16-pixel receptive fields, using eight kernels and a
stride of 2 leads to sparse reconstructions of comparable quality as using 512
kernels and a stride of 16 (the nonoverlapping case). We also find that for a
given stride and number of kernels, the patch size does not significantly
affect reconstruction quality. Instead, the learned convolution kernels have a
natural support radius independent of the patch size.

本文研究了使用改进的 LCA 算法的一种 DCN 类型，探讨了卷积核数量、步长、感受野大小对重建质量的影响，发现使用八个卷积核和步长为 2 的情况下可以产生与 512 个卷积核、步长为 16 的情况下相当的稀疏重建质量，此外发现在给定的步长和卷积核数量下，路径大小不显著影响重建质量。