89 research outputs found
Two-dimensional approximately harmonic projection for gait recognition
This paper presents a two-dimensional approximately harmonic projection (2DAHP) algorithm for gait recognition. 2DAHP is originated from the approximately harmonic projection (AHP), while 2DAHP offers some advantages over AHP. 1) 2DAHP can preserve the local geometrical structure and cluster structure of image data as AHP. 2) 2DAHP encodes images as matrices or second-order tensors rather than one-dimensional vectors, so 2DAHP can keep the correlation among different coordinates of image data. 3) 2DAHP avoids the singularity problem suffered by AHP. 4) 2DAHP runs faster than AHP. Extensive experiments on gait recognition show the effectiveness and efficiency of the proposed method
Calculation and Analysis of the Instream Ecological Flow for the Irtysh River
AbstractInstream ecological flow is essential determinant of river health. In this paper, the monthly minimum flow calculation method, the (new) monthly frequency calculation method were applied to calculate and evaluate the minimum instream ecological flow and the optimal instream ecological flow for the Irtysh River, and the different criteria of instream ecological flow was calculated by the improved Tennant method. It is expected to provide a scientific basis for the reasonable allocation of water resource in Irtysh River basin by calculating the instream ecological flow
Theory Research on Evolution and Protection of River Ecosystem under the Influence of Human Activities
Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive
Hydrological Variation Characteristics of Rivers in Humid Region: Oujiang River, China
AbstractOujiang River was selected as the case study, and a dataset of daily flow series at Xuren Station was used to explore the hydrologic characteristics of rivers in humid areas, by using the ‘Indicators of Hydrologic Alteration’ approach and ‘Range of Variability Approach’. Results showed that the overall alteration of the hydrological regime for Oujiang River belonged to the low alteration category, and some key eco-hydrological characteristics should be protected in certain key periods to maintain the integrality and health status of river ecosystems
Direct Adversarial Training: A New Approach for Stabilizing The Training Process of GANs
Generative Adversarial Networks (GANs) are the most popular models for image
generation by optimizing discriminator and generator jointly and gradually.
However, instability in training process is still one of the open problems for
all GAN-based algorithms. In order to stabilize training, some regularization
and normalization techniques have been proposed to make discriminator meet the
Lipschitz continuity constraint. In this paper, a new approach inspired by
works on adversarial attack is proposed to stabilize the training process of
GANs. It is found that sometimes the images generated by the generator play a
role just like adversarial examples for discriminator during the training
process, which might be a part of the reason of the unstable training. With
this discovery, we propose to introduce a adversarial training method into the
training process of GANs to improve its stabilization. We prove that this DAT
can limit the Lipschitz constant of the discriminator adaptively. The advanced
performance of the proposed method is verified on multiple baseline and SOTA
networks, such as DCGAN, WGAN, Spectral Normalization GAN, Self-supervised GAN
and Information Maximum GAN
A Comprehensive Survey on Data-Efficient GANs in Image Generation
Generative Adversarial Networks (GANs) have achieved remarkable achievements
in image synthesis. These successes of GANs rely on large scale datasets,
requiring too much cost. With limited training data, how to stable the training
process of GANs and generate realistic images have attracted more attention.
The challenges of Data-Efficient GANs (DE-GANs) mainly arise from three
aspects: (i) Mismatch Between Training and Target Distributions, (ii)
Overfitting of the Discriminator, and (iii) Imbalance Between Latent and Data
Spaces. Although many augmentation and pre-training strategies have been
proposed to alleviate these issues, there lacks a systematic survey to
summarize the properties, challenges, and solutions of DE-GANs. In this paper,
we revisit and define DE-GANs from the perspective of distribution
optimization. We conclude and analyze the challenges of DE-GANs. Meanwhile, we
propose a taxonomy, which classifies the existing methods into three
categories: Data Selection, GANs Optimization, and Knowledge Sharing. Last but
not the least, we attempt to highlight the current problems and the future
directions.Comment: Under revie
A Systematic Survey of Regularization and Normalization in GANs
Generative Adversarial Networks (GANs) have been widely applied in different
scenarios thanks to the development of deep neural networks. The original GAN
was proposed based on the non-parametric assumption of the infinite capacity of
networks. However, it is still unknown whether GANs can generate realistic
samples without any prior information. Due to the overconfident assumption,
many issues remain unaddressed in GANs' training, such as non-convergence, mode
collapses, gradient vanishing. Regularization and normalization are common
methods of introducing prior information to stabilize training and improve
discrimination. Although a handful number of regularization and normalization
methods have been proposed for GANs, to the best of our knowledge, there exists
no comprehensive survey which primarily focuses on objectives and development
of these methods, apart from some in-comprehensive and limited scope studies.
In this work, we conduct a comprehensive survey on the regularization and
normalization techniques from different perspectives of GANs training. First,
we systematically describe different perspectives of GANs training and thus
obtain the different objectives of regularization and normalization. Based on
these objectives, we propose a new taxonomy. Furthermore, we compare the
performance of the mainstream methods on different datasets and investigate the
regularization and normalization techniques that have been frequently employed
in SOTA GANs. Finally, we highlight potential future directions of research in
this domain
Multiview Discriminative Geometry Preserving Projection for Image Classification
In many image classification applications, it is common to extract multiple visual features from different views to describe an image. Since different visual features have their own specific statistical properties and discriminative powers for image classification, the conventional solution for multiple view data is to concatenate these feature vectors as a new feature vector. However, this simple concatenation strategy not only ignores the complementary nature of different views, but also ends up with “curse of dimensionality.” To address this problem, we propose a novel multiview subspace learning algorithm in this paper, named multiview discriminative geometry preserving projection (MDGPP) for feature extraction and classification. MDGPP can not only preserve the intraclass geometry and interclass discrimination information under a single view, but also explore the complementary property of different views to obtain a low-dimensional optimal consensus embedding by using an alternating-optimization-based iterative algorithm. Experimental results on face recognition and facial expression recognition demonstrate the effectiveness of the proposed algorithm
Semisupervised Kernel Marginal Fisher Analysis for Face Recognition
Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper. SKMFA can make use of both labelled and unlabeled samples to learn the projection matrix for nonlinear dimensionality reduction. Meanwhile, it can successfully avoid the singularity problem by not calculating the matrix inverse. In addition, in order to make the nonlinear structure captured by the data-dependent kernel consistent with the intrinsic manifold structure, a manifold adaptive nonparameter kernel is incorporated into the learning process of SKMFA. Experimental results on three face image databases demonstrate the effectiveness of our proposed algorithm
- …