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View all- Singh UAbhishek KAzad H(2024)A Survey of Cutting-edge Multimodal Sentiment AnalysisACM Computing Surveys10.1145/365214956:9(1-38)Online publication date: 25-Apr-2024
PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters
Pages 3 - 15
Abstract
Due to the sparsity of features, noise has proven to be a great inhibitor in the classification of handwritten characters. To combat this, most techniques perform denoising of the data before classification. In this paper, we consolidate the approach by training an all-in-one model that is able to classify even noisy characters. For classification, we progressively train a classifier generative adversarial network on the characters from low to high resolution. We show that by learning the features at each resolution independently a trained model is able to accurately classify characters even in the presence of noise. We experimentally demonstrate the effectiveness of our approach by classifying noisy versions of MNIST [13], handwritten Bangla Numeral, and Basic Character datasets [5, 6].
References
[1]
Aref WG and Samet H Decomposing a window into maximal quadtree blocks Acta Inf. 1993 30 5 425-439
[2]
Basu, S., Ganguly, S., Mukhopadhyay, S., DiBiano, R., Karki, M., Nemani, R.R.: Deepsat: A learning framework for satellite imagery. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, Bellevue, WA, USA, 3–6 November 2015
[3]
Basu S et al. Learning sparse feature representations using probabilistic quadtrees and deep belief nets Neural Process. Lett. 2017 45 3 855-867
[4]
Basu, S., et al.: A theoretical analysis of deep neural networks for texture classification. In: 2016 International Joint Conference on Neural Networks, IJCNN 2016, Vancouver, BC, Canada, 24–29 July 2016, pp. 992–999 (2016)
[5]
Bhattacharya U and Chaudhuri BB Handwritten numeral databases of indian scripts and multistage recognition of mixed numerals IEEE Trans. Pattern Anal. Mach. Intell. 2009 31 3 444-457
[6]
Bhattacharya U, Shridhar M, Parui SK, Sen P, and Chaudhuri B Offline recognition of handwritten bangla characters: an efficient two-stage approach Pattern Anal. Appl. 2012 15 4 445-458
[7]
Boureau, Y.L., Cun, Y.L., et al.: Sparse feature learning for deep belief networks. In: Advances in Neural Information Processing Systems, pp. 1185–1192 (2008)
[8]
Collier, E., DiBiano, R., Mukhopadhyay, S.: CactusNets: Layer applicability as a metric for transfer learning. In: 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil, 8–13 July 2018, pp. 1–8 (2018)
[9]
Goodfellow, I., et al.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 27, pp. 2672–2680. Curran Associates, Inc. (2014). http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
[10]
Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 447–456 (2015)
[11]
Hariharan, B., Girshick, R.: Low-shot visual recognition by shrinking and hallucinating features. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3018–3027 (2017)
[12]
Huang K and Aviyente S Sparse representation for signal classification NIPS 2006 19 609-616
[13]
Karki, M., Liu, Q., DiBiano, R., Basu, S., Mukhopadhyay, S.: Pixel-level reconstruction and classification for noisy handwritten bangla characters. In: 16th International Conference on Frontiers in Handwriting Recognition, ICFHR 2018, Niagara Falls, NY, USA, 5–8 August 2018, pp. 511–516 (2018).
[14]
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. CoRR abs/1710.10196 (2017). http://arxiv.org/abs/1710.10196
[15]
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
[16]
LeCun Y et al. Backpropagation applied to handwritten zip code recognition Neural Comput. 1989 1 4 541-551
[17]
LeCun Y, Bottou L, Bengio Y, and Haffner P Gradient-based learning applied to document recognition Proc. IEEE 1998 86 11 2278-2324
[18]
Markas T and Reif J Quad tree structures for image compression applications Inf. Process. Manage. 1992 28 6 707-721
[19]
Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 427–436 (2015)
[20]
Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier GANs. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 2642–2651. JMLR. org (2017)
[21]
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2536–2544 (2016)
[22]
Su, J., Vargas, D.V., Sakurai, K.: One pixel attack for fooling deep neural networks. In: Arxiv (2018)
[23]
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320–3328 (2014)
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Nov 2019
321 pages
© Springer Nature Switzerland AG 2019.
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Published: 04 November 2019
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View all- Singh UAbhishek KAzad H(2024)A Survey of Cutting-edge Multimodal Sentiment AnalysisACM Computing Surveys10.1145/365214956:9(1-38)Online publication date: 25-Apr-2024