참고문헌
- H. Wang, "Garbage recognition and classification system based on convolutional neural network vgg16," in 2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEM-CSE). IEEE, 2020, pp. 252-255.
- Y. Liao, "A web-based dataset for garbage classification based on shanghai's rule," International Journal of Machine Learning and Computing, vol. 10, no. 4, 2020.
- M. Zeng, X. Lu, W. Xu, T. Zhou, and Y. Liu, "Public garbagenet: A deep learning framework for public garbage classification," in 2020 39th Chinese Control Conference (CCC). IEEE, 2020, pp. 7200-7205.
- L. Cao and W. Xiang, "Application of convolutional neural network based on transfer learning for garbage classification," in 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC).IEEE, 2020, pp. 1032-1036.
- R. Sidharth, P. Rohit, S. Vishagan, R. Karthika, and M. Ganesan, "Deep learning based smart garbage classifier for effective waste management," in2020 5th Inter-national Conference on Communication and ElectronicsSystems (ICCES). IEEE, 2020, pp. 1086-1089.
- M. Yang and G. Thung, "Classification of trash for recyclability status," CS229 Project Report, vol. 2016,2016.
- R. A. Aral, S. R. Keskin, M. Kaya, and M. Haciomeroglu, "Classification of trashnet dataset based on deep learning models," in 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018, pp. 2058-2062.
- S. L. Rabano, M. K. Cabatuan, E. Sybingco, E. P. Dadios, and E. J. Calilung, "Common garbage classification using mobilenet," in2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM). IEEE, 2018, pp. 1-4.
- U. Ozkaya and L. Seyfi, "Fine-tuning models comparisons on garbage classification for recyclability," arXivpreprint arXiv:1908.04393, 2019.
- A. H. Vo, M. T. Vo, T. Leet al., "A novel framework for trash classification using deep transfer learning," IEEEAccess, vol. 7, pp. 178 631-178 639, 2019.
- S. Albawi, T. A. Mohammed, and S. Al-Zawi, "Understanding of a convolutional neural network," in2017International Conference on Engineering and Technology(ICET). Ieee, 2017, pp. 1-6.
- I. Kandel and M. Castelli, "Transfer learning with convolutional neural networks for diabetic retinopathy image classification. a review," Applied Sciences, vol. 10, no. 6,p. 2021, 2020. https://doi.org/10.3390/app10062021
- L. Torrey and J. Shavlik, "Transfer learning," in Handbook of research on machine learning applications and trends: algorithms, methods, and techniques. IGI Global,2010, pp. 242-264.
- J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," in2009 IEEE conference on computer vision and pattern recognition. Ieee, 2009, pp. 248-255.
- C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, andZ. Wojna, "Rethinking the inception architecture for computer vision," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp.2818-2826.
- F. Chollet, "Xception: Deep learning with depthwise separable convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition,2017, pp. 1251-1258.
- K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXivpreprint arXiv:1409.1556, 2014.
- K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
- I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu,D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative adversarial networks," arXiv preprintarXiv:1406.2661, 2014.
- T. Karras, S. Laine, and T. Aila, "A style-based generator architecture for generative adversarial networks," in Proceedings of the IEEE/CVF Conference on ComputerVision and Pattern Recognition, 2019, pp. 4401-4410.
- G. Thung and M. Yang, "Trashnet," GitHub repository,2016.
- A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury,G. Chanan, T. Killeen, Z. Lin, N. Gimelshein,L.Antiga, A.Desmaison, A.Kopf, E.Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, "Pytorch: An imperative style, high-performance deep learning library," in Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch e-Buc, E. Fox, and R. Garnett, Eds.Curran Associates, Inc., 2019, pp. 8024-8035. [Online]. Available: http://papers.neurips.cc/paper/9015-pytorch-animperative-style-high-performance-deep-learninglibrary.pdf
- Z. Hussain, F. Gimenez, D. Yi, and D. Rubin, "Differential data augmentation techniques for medical imaging classification tasks," in AMIA Annual Symposium Proceedings, vol. 2017.American Medical InformaticsAssociation, 2017, p. 979.
- A. Mikolajczyk and M. Grochowski, "Data augmentation for improving deep learning in image classification problem," in2018 international interdisciplinary PhD workshop (IIPhDW). IEEE, 2018, pp. 117-12