Abstract
In recent years, multitask learning has turned out to be of great success in various applications. Though single task model training has promised great results throughout these years, it ignores valuable information which might help in estimating better learning parameters. By virtue of learning-related tasks, multitask learning has been able to generalize the models even better. In this paper, we try to enhance the feature mapping of multitask deep learning models by sharing features among related tasks and inductive transfer learning. We have explored the learning of task relationship among various tasks for acquiring more benefits from multitask learning. The proposed enhanced model is compared with two state-of-the-art multitask deep learning models, namely Hyperface and FaceNet. The results show better performance of the proposed model in predicting age, gender and ethnicity on the UTK face dataset.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agarwal A, Triggs W (2008) Multilevel image coding with hyperfeatures. Int J Comput Vis 78 (06 2008). https://doi.org/10.1007/s11263-007-0072-x
Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Hasan M, Van Essen BC, Awwal AA, Asari VK (2019) A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3):292
Amyar A, Modzelewski R, Ruan S (2020) Multi-task deep learning based ct imaging analysis for covid-19: Classification and segmentation. medRxiv
Das A, Dantcheva A, Bremond F (2018) Mitigating bias in gender, age and ethnicity classification: a multi-task convolution neural network approach. In: Proceedings of the European conference on computer vision (ECCV). pp 0
Fourure D, Emonet R, Fromont E, Muselet D, Neverova N, Trémeau A, Wolf C (2017) Multi-task, multi-domain learning: application to semantic segmentation and pose regression. Neurocomputing 251:68–80
Ke R, Bugeau A, Papadakis N, Kirkland M, Schuetz P (2020) Schönlieb. Multi-task deep learning for image segmentation using recursive approximation tasks, C.B
Kong K, Lee J, Song W, Kang M, Kwon K, Kim SG (2019) Multitask bilateral learning for real-time image enhancement. J Soc Inf Displ 27. https://doi.org/10.1002/jsid.791
Le TLT, Thome N, Bernard S, Bismuth V, Patoureaux F (2019) Multitask classification and segmentation for cancer diagnosis in mammography. arXiv:1909.05397
Li Y, Tian X, Liu T, Tao D (2015) Multi-task model and feature joint learning. In: Twenty-fourth international joint conference on artificial intelligence
Long M, Cao Z, Wang J, Yu PS (2015) Learning multiple tasks with multilinear relationship networks. arXiv:1506.02117
Moeskops P, Wolterink JM, van der Velden BH, Gilhuijs KG, Leiner T, Viergever MA, Išgum I (2016) Deep learning for multi-task medical image segmentation in multiple modalities. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 478–486
Ranjan R, Patel VM, Chellappa R (2017) Hyperface: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Trans Pattern Anal Mach Intell 41(1):121–135
Wang N, Christodoulou AG, Xie Y, Wang Z, Deng Z, Zhou B, Lee S, Fan Z, Chang H, Yu W, Li D (April 2019) Quantitative 3d dynamic contrast-enhanced (dce) mr imaging of carotid vessel wall by fast t1 mapping using multitasking. Mag Resonan Med 81(4):2302–2314
Wu X, Liang L, Shi Y, Geng Z, Fomel S (2019) Multitask learning for local seismic image processing: fault detection, structure-oriented smoothing with edge-preserving, and seismic normal estimation by using a single convolutional neural network. Geophys J Int 219(3):2097–2109
Yu B, Lane I (2014) Multi-task deep learning for image understanding. In: 2014 6th international conference of soft computing and pattern recognition (SoCPaR). IEEE, pp 37–42
Zhang, Zhifei SY, Qi H (2017) Age progression/regression by conditional adversarial autoencoder. In: IEEE conference on computer vision and pattern recognition (CVPR). IEEE
Zhang Y, Yang Q (2017) A survey on multi-task learning. arXiv:1707.08114
Acknowledgements
This work is supported by the Science and Engineering Board (SERB), Department of Science and Technology (DST) of the Government of India under Grant No. ECR/2018/000204 and Grant No. EEQ/2019/000657.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Biswas, A., Bora, A., Malakar, D., Chakraborty, S., Bera, S. (2022). Multitask Deep Learning Model with Efficient Encoding Layer and Enhanced Parallel Convolution Block. In: Giri, D., Raymond Choo, KK., Ponnusamy, S., Meng, W., Akleylek, S., Prasad Maity, S. (eds) Proceedings of the Seventh International Conference on Mathematics and Computing . Advances in Intelligent Systems and Computing, vol 1412. Springer, Singapore. https://doi.org/10.1007/978-981-16-6890-6_26
Download citation
DOI: https://doi.org/10.1007/978-981-16-6890-6_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6889-0
Online ISBN: 978-981-16-6890-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)