Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers

References

1 
Russakovsky Olga, December 2015, ImageNet Large Scale Visual Recognition Challenge(ILSVRC), International Journal of Computer Vision, Vol. 115, No. 3, pp. 211-252DOI
2 
A. Ahmed, K. Yu, W. Xu, Y. Gong, E. Xing, 2008, Training hierarchical feed-forward visual recognition models using transfer learning from pseudo-tasks, Proceedings of Euro- pean Conference on Computer Vision(ECCV), pp. 69-82DOI
3 
Hao Wang, Naiyan Wang, Dit-Yan Yeung, 2015 9, Collabora- tive deep learning for recommender systems, Proceedings of the 21th ACM SIGKDD, pp. 1235-1244DOI
4 
J. Yosinski, J. Clune, Y. Bengio, H. Lipson, 2014, How transferable are features indeep neural networks?, Advances in neural information processing systems, pp. 3320-3328Google Search
5 
C. Szegedy, S. Loffe, V. Vanhoucke, A. Alemi, 2017, Incep- tion-v4, Inception-ResNet and the Impact of Residual Connections on Learning, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp. 4278-4284Google Search
6 
K. He, X. Zhang, S. Ren, J. Sun, 2016, Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778Google Search
7 
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, 2016, Rethinking the inception architecture for computer vision, Proceedings of the IEEE Conference on computer vision and pattern recognition, pp. 2818-2826Google Search
8 
M. D. Zeiler, R. Fergus, 2014. 9, Visualizing and under- standing convolutional networks, European conference on computer vision, pp. 818-833DOI
9 
A. Krizhevsky, I. Sutskever, G. E. Hinton, 2012, Imagenet classification with deep convolutional neural network, Advances in neural information processing systems, pp. 1097-1105Google Search
10 
S. J. Pan, Q. Yang, 2010, A survey on transfer learning, IEEE Transactions on knowledge and data engineering, Vol. 22, No. 10, pp. 1345-1359DOI
11 
A. Canziani, A. Paszke, E. Culurciello, 2016, An analysis of deep neural network models for practical applications, arXiv preprint arXiv:1605.07678Google Search
12 
K. Simonyan, A. Zisserman, 2014, Very deep convolutional networks for large-scale imagerecognition, arXiv preprint arXiv:1409.1556Google Search
13 
Y. Tang, 2013, Deep learning using linear support vector machines, arXiv preprintarXiv:1306.0239Google Search
14 
M. Lin, Q. Chen, S. Yan, 2013, Network in network, arXiv preprint arXiv:1312.4400Google Search
15 
Balakrishnan Anusha, Dixit Kalpit, 2016, DeepPlaylist: Using Recurrent Neural Networks to Predict Song Similarity, https://cs224d.stanford.edu/reports/BalakrishnanDixit.pdfGoogle Search
16 
Jing Kevin, Visual Search using features extracted from Tensorflow inception model, https://github.com/jamesmgg/VisualSearchServerGoogle Search
17 
Lab Kernix, Image Classification with a Pre-trained Deep Neural Network, https://www.kernix.com/blog/image-classification-with-a-pre-trained-deep-neural-network_p11Google Search
18 
Thompson Scott, Using Transfer Learning to Classify Image with Tensorflow, https://medium.com/@st553/using-transfer-learning-to-classify-images-with-tensorflow-b0f3142b9366Google Search
19 
Google AI Blog, , Train Your Own Image Classifier with Inception in Tensorflow, https://research.googleblog.com/2016/03/train-your-own-image-classifier-with.htmlGoogle Search
20 
Raj Bharath, Data Augmentation - How to use Deep Learning when you have limited data - part 2, https://medium.com/nanonets/how-to-use-deep-learning-when-you-have-limited-data-part-2-data-augmentation-c26971dc8cedGoogle Search
21 
Marcelino Pedro, Transfer Learning from Pre-trained Models, https://towardsdatascience.com/transfer-learning-from-pre-trained-models-f2393f124751Google Search