There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to node classification. However, most existing works ignore the relation heterogeneity with multiplex network between multi-typed nodes and different importance of relations in meta-paths for node embedding, which can hardly capture the heterogeneous structure signals across different relations. To tackle this challenge, this work proposes a Multiplex Heterogeneous Graph Convolutional Network (MHGCN) for heterogeneous network embedding. Our MHGCN can automatically learn the useful heterogeneous meta-path interactions of different lengths in multiplex heterogeneous networks through multi-layer convolution aggregation. Additionally, we effectively integrate both multi-relation structural signals and attribute semantics into the learned node embeddings with both unsupervised and semi-supervised learning paradigms. Extensive experiments on five real-world datasets with various network analytical tasks demonstrate the significant superiority of MHGCN against state-of-the-art embedding baselines in terms of all evaluation metrics. The source code of our method is available at: https://github.com/NSSSJSS/MHGCN.

/pdf/multiplex-heterogeneous-graph-convolutional-network-2j3374uf.pdf

Multiplex Heterogeneous Graph Convolutional Network

Self-attention can capture long-distance dependencies and is widely used in semantic segmentation. Existing methods mainly use two kinds of self-attentions, i.e., spatial attention and channel attention, which can capture the relations in HW dimension (image plane, height and width) and C dimension (channels), respectively. Very little research investigates self-attention along other dimensions, which can potentially improve the segmentation performance. In this work, we investigate the self-attentions along all the possible dimensions {H,W,C,HW,HC,CW,HWC}. Then we explore the aggregation of all the possible self-attentions. We apply the neural architecture search (NAS) technique to achieve optimal aggregation. Specifically, we carefully design (1) the search space and (2) the optimization method. For (1), we introduce a building block, a basic self-attention search unit (BSU), which can model self-attentions along all the dimensions. And the search space contains within-BSU and cross-BSU operations. In addition, we propose an attention-map splitting method, which can reduce the computations by 1/3. For (2), we apply an efficient differentiable optimization method to search the optimal aggregation. We conduct extensive experiments on Cityscapes and ADE20K datasets. The results show the effectiveness of the proposed method, and we achieve very competitive performance against state-of-the-art methods. 

Self-attention neural architecture search for semantic image segmentation

In this paper, we propose a novel unsupervised deep learning model, called PCA-based Convolutional Network (PCN). The architecture of PCN is composed of several feature extraction stages and a nonlinear output stage. Particularly, each feature extraction stage includes two layers: a convolutional layer and a feature pooling layer. In the convolutional layer, the filter banks are simply learned by PCA. In the nonlinear output stage, binary hashing is applied. For the higher convolutional layers, the filter banks are learned from the feature maps that were obtained in the previous stage. To test PCN, we conducted extensive experiments on some challenging tasks, including handwritten digits recognition, face recognition and texture classification. The results show that PCN performs competitive with or even better than state-of-the-art deep learning models. More importantly, since there is no back propagation for supervised finetuning, PCN is much more efficient than existing deep networks.

A PCA-Based Convolutional Network.

Texture is normally represented by aggregating local features based on the assumption of spatial homogeneity. Effective texture features are always the research focus even though both hand-crafted and deep learning approaches have been extensively investigated. Motivated by the success of Bilinear Convolutional Neural Networks (BCNNs) in fine-grained image recognition, we propose to incorporate the BCNN with the Pair-wise Difference Pooling (i.e. BCNN-PDP) for texture classification. The BCNN-PDP is built on top of a set of feature maps extracted at a convolutional layer of the pre-trained CNN. Compared with the outer product used by the original BCNN feature set, the pair-wise difference not only captures the pair-wise relationship between two sets of features but also encodes the difference between each pair of features. Considering the importance of the gradient data to the representation of image structures, we further generalise the BCNN-PDP feature set to two sets of feature maps computed from the original image and its gradient magnitude map respectively, i.e. the Fused BCNN-PDP (F-BCNN-PDP) feature set. In addition, the BCNN-PDP can be applied to two different CNNs and is referred to as the Asymmetric BCNN-PDP (A-BCNN-PDP). The three PDP-based BCNN feature sets can also be extracted at multiple scales. Since the dimensionality of the BCNN feature vectors is very high, we propose a new yet simple Block-wise PCA (BPCA) method in order to derive more compact feature vectors. The proposed methods are tested on seven different datasets along with 21 baseline feature sets. The results show that the proposed feature sets are superior, or at least comparable, to their counterparts across different datasets.

Texture Classification Using Pair-Wise Difference Pooling-Based Bilinear Convolutional Neural Networks

Facial paralysis can be on one or both sides of face and one side is much more common. This disease can impose significant psychological and functional impairment to patients. Traditionally, patients with facial paralysis are evaluated and examined by physicians based on manually measurement of certain difference between the two facial sides. In this paper, we describe a new approach for quantitatively estimating the degree of facial paralysis. We first determine key points based on salient point detection. Then the differences between the two facial sides are calculated. The proposed method is proved to be useful and effective in practice.

Junyu Dong

Papers

Multiplex Heterogeneous Graph Convolutional Network

Self-attention neural architecture search for semantic image segmentation

A PCA-Based Convolutional Network.

Texture Classification Using Pair-Wise Difference Pooling-Based Bilinear Convolutional Neural Networks

An Approach for Quantitative Evaluation of the Degree of Facial Paralysis Based on Salient Point Detection