Importance Weighted Adversarial Nets for Partial Domain Adaptation

This paper proposes an importance weighted adversarial nets-based method for unsupervised domain adaptation, specific for partial domain adaptation where the target domain has less number of classes compared to the source domain. Previous domain adaptation methods generally assume the identical label spaces, such that reducing the distribution divergence leads to feasible knowledge transfer. However, such an assumption is no longer valid in a more realistic scenario that requires adaptation from a larger and more diverse source domain to a smaller target domain with less number of classes. This paper extends the adversarial nets-based domain adaptation and proposes a novel adversarial nets-based partial domain adaptation method to identify the source samples that are potentially from the outlier classes and, at the same time, reduce the shift of shared classes between domains

Investigation of Different Skeleton Features for CNN-based 3D Action Recognition

Deep learning techniques are being used in skeleton based action recognition tasks and outstanding performance has been reported. Compared with RNN based methods which tend to overemphasize temporal information, CNN-based approaches can jointly capture spatio-temporal information from texture color images encoded from skeleton sequences. There are several skeleton-based features that have proven effective in RNN-based and handcrafted-feature-based methods. However, it remains unknown whether they are suitable for CNN-based approaches. This paper proposes to encode five spatial skeleton features into images with different encoding methods. In addition, the performance implication of different joints used for feature extraction is studied. The proposed method achieved state-of-the-art performance on NTU RGB+D dataset for 3D human action analysis. An accuracy of 75.32\% was achieved in Large Scale 3D Human Activity Analysis Challenge in Depth Videos

The infinity : Mobius band in fashion zero waste pattern cutting method : a thesis presented in partial fulfilment of the requirements for the degree of Master of Design at Massey University, Wellington, New Zealand

Figures 8 & 11 were removed for copyright reasons, but may be accessed via Townsend & Mills, 2013 (Figs 8, 7 and 6) & Swann 2002 (Fig 1) respectively.The textile and fashion manufacturing industry entered a period of rapid development after the Industrial Revolution and the ensuing environmental problems have become increasingly serious. Fabric waste made by both traditional industry production processes and consumer behaviour has contributed to these problems. In recent years, fashion design practitioners have looked to zero waste pattern cutting methods as a response to fabric waste issues today. This practice-led fashion design research project investigates zero waste pattern cutting using a Mobius band to eliminate fabric waste, reducing manufacturing with minimal cutting lines and extending the life of the fabric. My design process, utilising action research, begins with analysing existing zero waste pattern cutting methods and explores the Mobius band to continuously develop and generate innovative garment shapes and test feasibility in varied fabrics, size, and dimension. This creative method aims to explore the design potential for innovative diverse shapes and multiple wear possibilities to meet the individual needs of the 'new' consumer

New Interpretations of Normalization Methods in Deep Learning

In recent years, a variety of normalization methods have been proposed to help train neural networks, such as batch normalization (BN), layer normalization (LN), weight normalization (WN), group normalization (GN), etc. However, mathematical tools to analyze all these normalization methods are lacking. In this paper, we first propose a lemma to define some necessary tools. Then, we use these tools to make a deep analysis on popular normalization methods and obtain the following conclusions: 1) Most of the normalization methods can be interpreted in a unified framework, namely normalizing pre-activations or weights onto a sphere; 2) Since most of the existing normalization methods are scaling invariant, we can conduct optimization on a sphere with scaling symmetry removed, which can help stabilize the training of network; 3) We prove that training with these normalization methods can make the norm of weights increase, which could cause adversarial vulnerability as it amplifies the attack. Finally, a series of experiments are conducted to verify these claims.Comment: Accepted by AAAI 202

Learning Inter- and Intra-frame Representations for Non-Lambertian Photometric Stereo

In this paper, we build a two-stage Convolutional Neural Network (CNN) architecture to construct inter- and intra-frame representations based on an arbitrary number of images captured under different light directions, performing accurate normal estimation of non-Lambertian objects. We experimentally investigate numerous network design alternatives for identifying the optimal scheme to deploy inter-frame and intra-frame feature extraction modules for the photometric stereo problem. Moreover, we propose to utilize the easily obtained object mask for eliminating adverse interference from invalid background regions in intra-frame spatial convolutions, thus effectively improve the accuracy of normal estimation for surfaces made of dark materials or with cast shadows. Experimental results demonstrate that proposed masked two-stage photometric stereo CNN model (MT-PS-CNN) performs favorably against state-of-the-art photometric stereo techniques in terms of both accuracy and efficiency. In addition, the proposed method is capable of predicting accurate and rich surface normal details for non-Lambertian objects of complex geometry and performs stably given inputs captured in both sparse and dense lighting distributions.Comment: 9 pages,8 figure

A curve model for association of serum homocysteine with carotid artery hemodynamics

Purpose: To investigate the correlation between carotid artery hemodynamics and serum homocysteine.Methods: A total of 894 participants made up of 439 male (49.11 %) and 455 female (50.89 %) from Ma’anshan, China, enrolled in the cross-sectional study. Data collection included demographics, blood sample and carotid ultrasonography. Piecewise linear regression analysis was used to analyze the relationship between serum homocysteine and carotid artery hemodynamics.Results: Homocysteine (Hcy) levels were divided into four groups by quartiles. The populations of the groups were 226, 220, 222, 226; and their mean ages were 56.52 ± 10.49, 62.27 ± 10.06, 63.42 ± 9.81 and 65.38 ± 10.56 years, respectively. After adjustment for blood biochemical and demographics factors, U-shaped and S-shaped curves were as observed between Hcy and carotid artery hemodynamics. The adjusted regression analysis showed that the threshold values of Hcy with end diastolic velocity (EDV) of right common carotid artery (CCA) were 12.50 and 19.00, while for the EDV of right internal carotid artery (ICA), the values were 11.50 and 22.00. U-shaped curves were observed between Hcy and peak systolic velocity (PSV) of left CCA, EDV of left CCA, PSV of left ICA and EDV of left ICA. The threshold values of Hcy with PSV of left CCA, EDV of left CCA, PSV of left ICA and EDV of left ICA were 14.00, 14.00, 14.00 and 13.50, respectively.Conclusion: These results indicate that a significant correlation exists between homocysteine at different concentrations and carotid artery hemodynamics.Keywords: Homocysteine, Hemodynamics, End diastolic velocity, Peak systolic velocit