Chinese Painting Style Transfer System Based on Machine Learning

2021 ◽  
Author(s):  
Zhigang Li ◽  
Shiwei Lin ◽  
Yixuan Peng
Keyword(s):  
Machine Learning ◽  
Chinese Painting ◽  
Transfer System ◽  
Style Transfer ◽  
Painting Style Transfer

Transfer learning based convolution neural net for authentication and classification of emotions from natural and stimulated speech signals

2021 ◽  
pp. 1-12
Author(s):  
Mukul Kumar ◽  
Nipun Katyal ◽  
Nersisson Ruban ◽  
Elena Lyakso ◽  
A. Mary Mekala ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Research Work ◽  
Oral Communication ◽  
Neural Net ◽  
Emotion Classification ◽  
Style Transfer ◽  
Conventional Machine ◽  
Neural Network Algorithm

Over the years the need for differentiating various emotions from oral communication plays an important role in emotion based studies. There have been different algorithms to classify the kinds of emotion. Although there is no measure of fidelity of the emotion under consideration, which is primarily due to the reason that most of the readily available datasets that are annotated are produced by actors and not generated in real-world scenarios. Therefore, the predicted emotion lacks an important aspect called authenticity, which is whether an emotion is actual or stimulated. In this research work, we have developed a transfer learning and style transfer based hybrid convolutional neural network algorithm to classify the emotion as well as the fidelity of the emotion. The model is trained on features extracted from a dataset that contains stimulated as well as actual utterances. We have compared the developed algorithm with conventional machine learning and deep learning techniques by few metrics like accuracy, Precision, Recall and F1 score. The developed model performs much better than the conventional machine learning and deep learning models. The research aims to dive deeper into human emotion and make a model that understands it like humans do with precision, recall, F1 score values of 0.994, 0.996, 0.995 for speech authenticity and 0.992, 0.989, 0.99 for speech emotion classification respectively.

ABLE: A STANDING STYLE TRANSFER SYSTEM FOR A PERSON WITH DISABLED LOWER LIMBS

2010 ◽  
Author(s):  
YOSHIKAZU MORI ◽  
KEN MAEJIMA ◽  
KOUSUKE INOUE ◽  
NAOJI SHIROMA ◽  
YASUHIRO FUKUOKA
Keyword(s):  
Lower Limbs ◽  
Transfer System ◽  
Style Transfer

scholarly journals Development of a Standing Style Transfer System ABLE with Novel Crutches for a Person with Disabled Lower Limbs

2011 ◽  
Vol 5 (1) ◽  
pp. 83-93 ◽  
Author(s):  
Yoshikazu MORI ◽  
Toshiya TANIGUCHI ◽  
Kousuke INOUE ◽  
Yasuhiro FUKUOKA ◽  
Naoji SHIROMA
Keyword(s):  
Lower Limbs ◽  
Transfer System ◽  
Style Transfer

scholarly journals Pipes of AI – Machine Learning Assisted 3D Modeling Design

2021 ◽  
pp. 17-26
Author(s):  
Chuan Liu ◽  
Jiaqi Shen ◽  
Yue Ren ◽  
Hao Zheng
Keyword(s):  
Machine Learning ◽  
3D Modeling ◽  
Architectural Design ◽  
Early Stage ◽  
Input Image ◽  
Structure Design ◽  
Design Tool ◽  
Design Technique ◽  
Style Transfer ◽  
Floor Plans

AbstractStyle transfer is a design technique that is based on Artificial Intelligence and Machine Learning, which is an innovative way to generate new images with the intervention of style images. The output image will carry the characteristic of style image and maintain the content of the input image. However, the design technique is employed in generating 2D images, which has a limited range in practical use. Thus, the goal of the project is to utilize style transfer as a toolset for architectural design and find out the possibility for a 3D modeling design. To implement style transfer into the research, floor plans of different heights are selected from a given design boundary and set as the content images, while a framework of a truss structure is set as the style image. Transferred images are obtained after processing the style transfer neural network, then the geometric images are translated into floor plans for new structure design. After the selection of the tilt angle and the degree of density, vertical components that connecting two adjacent layers are generated to be the pillars of the structure. At this stage, 2D style transferred images are successfully transformed into 3D geometries, which can be applied to the architectural design processes. Generally speaking, style transfer is an intelligent design tool that provides architects with a variety of choices of idea-generating. It has the potential to inspire architects at an early stage of design with not only 2D but also 3D format.

scholarly journals Interactive Artistic Multi-style Transfer

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Xiaohui Wang ◽  
Yiran Lyu ◽  
Junfeng Huang ◽  
Ziying Wang ◽  
Jingyan Qin
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Deep Neural Networks ◽  
Subjective Evaluation ◽  
User Interaction ◽  
Evaluation Framework ◽  
Transfer System ◽  
Style Transfer ◽  
Artistic Style ◽  
Artistic Styles

AbstractArtistic style transfer is to render an image in the style of another image, which is a challenge problem in both image processing and arts. Deep neural networks are adopted to artistic style transfer and achieve remarkable success, such as AdaIN (adaptive instance normalization), WCT (whitening and coloring transforms), MST (multimodal style transfer), and SEMST (structure-emphasized multimodal style transfer). These algorithms modify the content image as a whole using only one style and one algorithm, which is easy to cause the foreground and background to be blurred together. In this paper, an iterative artistic multi-style transfer system is built to edit the image with multiple styles by flexible user interaction. First, a subjective evaluation experiment with art professionals is conducted to build an open evaluation framework for style transfer, including the universal evaluation questions and personalized answers for ten typical artistic styles. Then, we propose the interactive artistic multi-style transfer system, in which an interactive image crop tool is designed to cut a content image into several parts. For each part, users select a style image and an algorithm from AdaIN, WCT, MST, and SEMST by referring to the characteristics of styles and algorithms summarized by the evaluation experiments. To obtain richer results, the system provides a semantic-based parameter adjustment mode and the function of preserving colors of content image. Finally, case studies show the effectiveness and flexibility of the system.

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