Task Weighting through Gradient Projection for Multitask Learning

Caruana, R.: Multitask learning. Machine Learning 28

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Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 7482–7491 (2017), https://api.semanticscholar.org/CorpusID:4800342

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Rosenbaum, C., Klinger, T., Riemer, M.: Routing networks: Adaptive selection of non-linear functions for multi-task learning (2017), https://arxiv.org/abs/1711.01239

Original

2017

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Chen, Z., Badrinarayanan, V., Lee, C.Y., Rabinovich, A.: GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks (Jun 2018), http://arxiv.org/abs/1711.02257 , arXiv:1711.02257 [cs]

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Guo, M., Haque, A., Huang, D.A., Yeung, S., Fei-Fei, L.: Dynamic Task Prioritization for Multitask Learning. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018, vol. 11220, pp. 282–299. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-030-01270-0_17, https://link.springer.com/10.1007/978-3-030-01270-0_17 , series Title: Lecture Notes in Computer Science

2018

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Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 42

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Sener, O., Koltun, V.: Multi-task learning as multi-objective optimization. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 31. Curran Associates, Inc. (2018), https://proceedings.neurips.cc/paper_files/paper/2018/file/432aca3a1e345e339f35a30c8f65edce-Paper.pdf

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Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., Beijbom, O.: nuscenes: A multimodal dataset for autonomous driving. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 11618–11628. IEEE Computer Society, Los Alamitos, CA, USA (jun 2020). https://doi.org/10.1109/CVPR42600.2020.01164, https://doi.ieeecomputersociety.org/10.1109/CVPR42600.2020.01164

2020

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Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., Finn, C.: Gradient surgery for multi-task learning. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20, Curran Associates Inc., Red Hook, NY, USA (2020)

2020

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Liu, B., Liu, X., Jin, X., Stone, P., Liu, Q.: Conflict-Averse Gradient Descent for Multi-task learning. In: Advances in Neural Information Processing Systems. vol. 34, pp. 18878–18890. Curran Associates, Inc. (2021), https://proceedings.neurips.cc/paper/2021/hash/9d27fdf2477ffbff837d73ef7ae23db9-Abstract.html

2021

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Li, Z., Wang, W., Li, H., Xie, E., Sima, C., Lu, T., Qiao, Y., Dai, J.: Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision – ECCV 2022. pp. 1–18. Springer Nature Switzerland, Cham (2022)

2022

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