Acknowledgement
이 논문은 2021년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임(NRF-2021S1A5A2A01061459). 이 논문은 과학기술정보통신부와 정보통신산업진흥원의 '고성능 컴퓨팅 지원' 사업의 지원을 받아 수행하였음.
References
- T. Kim and N. Kim, "Deep learning-based professional image interpretation using expertise transplant," Journal of Intelligence and Information Systems, Vol.26, No.2, pp.79-104, 2020. https://doi.org/10.13088/JIIS.2020.26.2.079
- H. Kim, H. S. Oh, and D. Kim, "CNN architecture predicting movie rating from audience's reviews written in Korean," KIPS Transactions on Computer and Communication Systems, Vol.9, No.1, pp.17-24, 2020. https://doi.org/10.3745/KTCCS.2020.9.1.17
- A. Gijsenij, T. Gevers, and J. V. Weijer, "Computational color constancy: Survey and experiments," IEEE Transactions on Image Processing, Vol.20, No.9, pp.2475-2489, 2011. https://doi.org/10.1109/TIP.2011.2118224
- J. Xiao, S. Gu, and L. Zhang, "Multi-domain learning for accurate and few-shot color constancy," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.3258-3267, 2020.
- Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, and V. Lempitsky, "Domain-adversarial training of neural networks," Journal of Machine Learning Research, Vol.17, No.1, pp.2096-2030, 2016.
- G. Buchsbaum, "A spacial processor model for object color perception," Journal of the Franklin institute, Vol.310, No.1, pp.1-26, 1980. https://doi.org/10.1016/0016-0032(80)90058-7
- E. Land, "The retinex theory of color vision," Scientific American, Vol.237, No.6, pp.108-128, 1977. https://doi.org/10.1038/scientificamerican1277-108
- J. V. Weijer, T. Gevers, and A. Gijsenji, "Edge-based color constancy," IEEE Transactions on Image Processing, Vol.16, No.9, pp.2207-2214, 2007. https://doi.org/10.1109/TIP.2007.901808
- K. F. Yang, S. B. Gao, and Y. J. Li, "Efficient illuminant estimation for color constancy using grey pixels," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2254-2263, 2015.
- K. Barnard and G. Finlayson, "Shadow identification using colour ratios," in Proceedings of the IS&T's Color and Imaging Conference, Vol.2000, No.1, pp.97-101, 2000.
- B. Funt and W. Xiong, "Estimating illumination chromaticity via support vector regression," in Proceedings of the IS&T's Color and Imaging Conference, Vol.2004, No.1, pp.47-52, 2004.
- D. Cheng, B. Price, S. Cohen, and M. S. Brown, "Effective learning-based illuminant estimation using simple features," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1000-1008, 2015.
- S. Bianco, C. Cusano, and R. Schettini, "Color constancy using cnns," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.81-89, 2015.
- J. T. Barron, "Convolutional color constancy," in Proceedings of the IEEE International Conference on Computer Vision, pp.379-387, 2015.
- J. T Barron and Y. T. Tsai, "Fast fourier color constancy," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp.886-894, 2017.
- Y. Hu, B. Wang, and S. Lin, "Fc4: Fully convolutional color constancy with confidence weighted pooling," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.4085-4094, 2017.
- M. Rizzo, C. Conati, D. Jang, and H. Hu, "Cascading convolutional temporal colour constancy," arXiv preprint arXiv: 2106.07955, 2021.
- I. Goodfellow, J. P. Abadie, M. Mirza, B. Xu, D. W. Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative adversarial nets," Advances in Neural Information Processing Systems, pp.2672-2680, 2014.
- Y. Wang, F. Ma, Z. jin, Y. Yuan, G. Xun, K. Jha, L. Su, and J. Gao, "Eann: Event adversarial neural networks for multimodal fake news detection," in Proceedings of the 24th ACM SIGKDD International Conference On Knowledge Discovery & Data Mining, pp.849-857, 2018.
- D. Li, D. Chen, L. Shi, B. Jin, J. Goh, and S. K. Ng, "MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks," in International Conference on Artificial Neural Networks, Springer, pp.703-716, 2019.
- Z. Yang, J. Hu, R. Salakhutdinov, and W. Cohen, "Semi-supervised qa with generative domain-adaptive nets," arXiv preprint arXiv:1702.02206, 2017.
- Y. Ganin and V. Lempitsky, "Unsupervised domain adaptation by backpropagation," in International Conference on Machine Learning, pp.1180-1189, 2015.
- B. Sun, J. Feng, and K. Saenko, "Return of frustratingly easy domain adaptation," in Proceedings of the AAAI Conference on Artificial Intelligence, Vol.30, No.1, 2016.
- H. Yuzuguzel, "Learning colour constancy using convolutional neural networks," MS thesis, Tampere University of Technology, Finland, 2015.
- H. R. V. Joze, M. S. Drew, G. D. Finlayson, and P. A. T. Rey, "The role of bright pixels in illumination estimation," in Proceedings of the IS&T's Color and Imaging Conference, Vol.2012, No.1, pp.41-46, 2012.
- F. Laakom, J. Raitoharju, J. Nikkanen, A. Iosifidis, and M. Gabbouj, "Intel-tau: A color constancy dataset," IEEE Access, Vol.9, pp.39560-39567, 2021. https://doi.org/10.1109/ACCESS.2021.3064382