Predicting Multiple Pregrasping Poses by Combining Deep Convolutional Neural Networks with Mixture Density Networks

Moon, Sungphill; Park, Youngbin; Suh, Il Hong

doi:10.1007/978-3-319-46675-0_64

Cited by 3 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Convolutional Neural Network (CNN), which was originally conceived as a model of the brain, knowledge is obtained through the CNN learning process (Haykin 1994). CNN is an important improved multi-layer feed-forward neural network (Babu et al 2016;Li et al 2017;Moon et al 2016). In addition, the CNN has sparse connections, which can simplify network parameters compared with traditional neural networks.…”

Section: Estimation With Cnnmentioning

confidence: 99%

Photometric redshift estimation of galaxies with Convolutional Neural Network

Qiu

Zhang

et al. 2020

Res. Astron. Astrophys.

View full text Add to dashboard Cite

The abundant photometric data collected from multiple large-scale sky surveys give important opportunities for photometric redshift estimation. However, low accuracy is still a serious issue in the current photometric redshift estimation methods. In this paper, we propose a novel two-stage approach by integration of Self Organizing Map (SOM) and Convolutional Neural Network (CNN) methods together. The SOM-CNN method is tested on the dataset of 150 000 galaxies from Sloan Digital Sky Survey Data Release 13 (SDSS-DR13). In the first stage, we apply the SOM algorithm to photometric data clustering and divide the samples into early-type and late-type. In the second stage, the SOM-CNN model is established to estimate the photometric redshifts of galaxies. Next, the precision rate and recall rate curves (PRC) are given to evaluate the models of SOM-CNN and Back Propagation (BP). It can been seen from the PRC that the SOM-CNN model is better than BP, and the area of SOM-CNN is 0.94, while the BP is 0.91. Finally, we provide two key error indicators: mean square error (MSE) and Outliers. Our results show that the MSE of early-type is 0.0014 while late-type is 0.0019, which are better than the BP algorithm 22.2% and 26%, respectively. When compared with Outliers, our result is optimally 1.32%, while the K-nearest neighbor (KNN) algorithm has 3.93%. In addition, we also provide the error visualization figures about ΔZ and δ. According to the statistical calculations, the early-type with an error of less than 0.1 accounts for 98.86%, while the late-type is 99.03%. This result is better than those reported in the literature.

show abstract

Section: Estimation With Cnnmentioning

confidence: 99%

Photometric redshift estimation of galaxies with Convolutional Neural Network

Qiu

Zhang

et al. 2020

Res. Astron. Astrophys.

View full text Add to dashboard Cite

show abstract

“…A single MDN was used in [12], [4]. CNN+MDN is used in [9], [6]. CNN+RNN is used in [14], [2], [11].…”

Section: Extensions Of Mdnmentioning

confidence: 99%

A Hierarchical Mixture Density Network

Yang

Soriano

Kubo

et al. 2017

Neural Information Processing

View full text Add to dashboard Cite

The relationship among three correlated variables could be very sophisticated, as a result, we may not be able to find their hidden causality and model their relationship explicitly. However, we still can make our best guess for possible mappings among these variables, based on the observed relationship. One of the complicated relationships among three correlated variables could be a two-layer hierarchical many-to-many mapping. In this paper, we proposed a Hierarchical Mixture Density Network (HMDN) to model the two-layer hierarchical many-to-many mapping. We apply HMDN on an indoor positioning problem and show its benefit.

show abstract