14,832 research outputs found
A foundation for machine learning in design
This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, "What types of knowledge can be learnt?", "How does learning occur?", and "When does learning occur?". Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD
Distinguishing Computer-generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning
Computer-generated graphics (CGs) are images generated by computer software.
The~rapid development of computer graphics technologies has made it easier to
generate photorealistic computer graphics, and these graphics are quite
difficult to distinguish from natural images (NIs) with the naked eye. In this
paper, we propose a method based on sensor pattern noise (SPN) and deep
learning to distinguish CGs from NIs. Before being fed into our convolutional
neural network (CNN)-based model, these images---CGs and NIs---are clipped into
image patches. Furthermore, three high-pass filters (HPFs) are used to remove
low-frequency signals, which represent the image content. These filters are
also used to reveal the residual signal as well as SPN introduced by the
digital camera device. Different from the traditional methods of distinguishing
CGs from NIs, the proposed method utilizes a five-layer CNN to classify the
input image patches. Based on the classification results of the image patches,
we deploy a majority vote scheme to obtain the classification results for the
full-size images. The~experiments have demonstrated that (1) the proposed
method with three HPFs can achieve better results than that with only one HPF
or no HPF and that (2) the proposed method with three HPFs achieves 100\%
accuracy, although the NIs undergo a JPEG compression with a quality factor of
75.Comment: This paper has been published by Sensors. doi:10.3390/s18041296;
Sensors 2018, 18(4), 129
Exploring the structure of a real-time, arbitrary neural artistic stylization network
In this paper, we present a method which combines the flexibility of the
neural algorithm of artistic style with the speed of fast style transfer
networks to allow real-time stylization using any content/style image pair. We
build upon recent work leveraging conditional instance normalization for
multi-style transfer networks by learning to predict the conditional instance
normalization parameters directly from a style image. The model is successfully
trained on a corpus of roughly 80,000 paintings and is able to generalize to
paintings previously unobserved. We demonstrate that the learned embedding
space is smooth and contains a rich structure and organizes semantic
information associated with paintings in an entirely unsupervised manner.Comment: Accepted as an oral presentation at British Machine Vision Conference
(BMVC) 201
Malaria Parasitic Detection using a New Deep Boosted and Ensemble Learning Framework
Malaria is a potentially fatal plasmodium parasite injected by female
anopheles mosquitoes that infect red blood cells and millions worldwide yearly.
However, specialists' manual screening in clinical practice is laborious and
prone to error. Therefore, a novel Deep Boosted and Ensemble Learning (DBEL)
framework, comprising the stacking of new Boosted-BR-STM convolutional neural
networks (CNN) and the ensemble ML classifiers, is developed to screen malaria
parasite images. The proposed Boosted-BR-STM is based on a new
dilated-convolutional block-based split transform merge (STM) and feature-map
Squeezing-Boosting (SB) ideas. Moreover, the new STM block uses regional and
boundary operations to learn the malaria parasite's homogeneity, heterogeneity,
and boundary with patterns. Furthermore, the diverse boosted channels are
attained by employing Transfer Learning-based new feature-map SB in STM blocks
at the abstract, medium, and conclusion levels to learn minute intensity and
texture variation of the parasitic pattern. The proposed DBEL framework
implicates the stacking of prominent and diverse boosted channels and provides
the generated discriminative features of the developed Boosted-BR-STM to the
ensemble of ML classifiers. The proposed framework improves the discrimination
ability and generalization of ensemble learning. Moreover, the deep feature
spaces of the developed Boosted-BR-STM and customized CNNs are fed into ML
classifiers for comparative analysis. The proposed DBEL framework outperforms
the existing techniques on the NIH malaria dataset that are enhanced using
discrete wavelet transform to enrich feature space. The proposed DBEL framework
achieved Accuracy (98.50%), Sensitivity (0.9920), F-score (0.9850), and AUC
(0.997), which suggest it to be utilized for malaria parasite screening.Comment: 26 pages, 10 figures, 9 Table
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