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

비만아 부모의 돌봄 경험: q 방법론

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Ensemble methods are considered the state‐of‐the art solution for many machine learning challenges. Such methods improve the predictive performance of a single model by training multiple models and combining their predictions. This paper introduce the concept of ensemble learning, reviews traditional, novel and state‐of‐the‐art ensemble methods and discusses current challenges and trends in the field.

Ensemble learning: A survey

The need for appropriate ways to measure the distance or similarity between data is ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such good metrics for specific problems is generally difficult. This has led to the emergence of metric learning, which aims at automatically learning a metric from data and has attracted a lot of interest in machine learning and related fields for the past ten years. This survey paper proposes a systematic review of the metric learning literature, highlighting the pros and cons of each approach. We pay particular attention to Mahalanobis distance metric learning, a well-studied and successful framework, but additionally present a wide range of methods that have recently emerged as powerful alternatives, including nonlinear metric learning, similarity learning and local metric learning. Recent trends and extensions, such as semi-supervised metric learning, metric learning for histogram data and the derivation of generalization guarantees, are also covered. Finally, this survey addresses metric learning for structured data, in particular edit distance learning, and attempts to give an overview of the remaining challenges in metric learning for the years to come.

A Survey on Metric Learning for Feature Vectors and Structured Data

Generative adversarial networks with mixture of t-distributions noise for diverse image generation.

Tensor representation learning based image patch analysis for text identification and recognition

In recent years, many deep architectures have been proposed for handwritten text recognition. However, most of the previous deep models need large scale training data and a long training time to obtain good results. In this paper, we propose a novel deep learning method based on “stretching” the projection matrices of stacked feature learning models. We call the proposed method “stretching deep architectures” (or SDA). In the implementation of SDA, stacked feature learning models are first learned layer by layer, and then the stretching technique is applied on the weight matrices between successive layers. As the feature learning models can be efficiently optimized and the stretching results can be easily computed, the training of SDA is very fast and no back propagation is needed. We have tested SDA on handwritten digits recognition, Arabic subword recognition and English letter recognition tasks. Extensive experiments demonstrate that SDA performs not only better than shallow feature learning models, but also state-of-the-art deep learning models.

Stretching deep architectures for text recognition

Different wavelengths of light may undergo changes in underwater environment resulting in altered images. For example, the presence of floating particles causes underwater images to appear bluish and blurred. In this study, the authors propose a method called the deep sparse non-negative matrix factorisation (DSNMF) to estimate the illumination of an underwater image. The image under observation is divided into patches and each channel of a single patch is reshaped as an [ R, G, B ] matrix. The DSNMF method deeply factorises each input matrix into multiple layers with a sparseness constraint. The last layer of the factorised matrix is used as the illumination of the patch. The sparseness constraint adjusts the appearance of the final image. After factorisation, the estimated illumination is applied to each patch of the original image to obtain the final image. Compared with state-of-the-art underwater image enhancement methods using no reference image quality assessment, not only does the proposed method outperforms current techniques in terms of its visual effect and IQA, but is also simpler to implement.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2016.0543

Underwater image colour constancy based on DSNMF

Texture is an important property of images, and a key component for human visual perception. In this work, based on several feature learning models and deep architectures, we study the visual texture perception problem, which is helpful for understanding both the impact of texture itself and the basic mechanisms of human visual systems. Through a series of psychophysical experiments, we find that 12 perceptual features are significant to describe the texture images with regard to the human perceptions. Hence, we represent each texture image with a 12-dimensional vector, corresponding to the values of the 12 perceptual features. To improve the learnablity of existing feature learning models, we propose a set of deep architectures to learn compact representations of the texture perceptual features. Extensive experiments on texture images classification demonstrate the effectiveness of both the feature learning models and the deep architectures. In particular, the advantage of deep architectures over existing feature learning models is shown.

Guoqiang Zhong

Papers

Generative adversarial networks with mixture of t-distributions noise for diverse image generation.

Tensor representation learning based image patch analysis for text identification and recognition

Stretching deep architectures for text recognition

Underwater image colour constancy based on DSNMF

Visual Texture Perception with Feature Learning Models and Deep Architectures