By Jialong Wu.

I greatly appreciate those surveys below, which help me a lot. Please let me know if you find any mistakes or omissions!

• Survey
• Benchmark
  • Computer Vision
  • NLP
  • RL & Robotics
  • Graph
  • Recommendation
• Codebase
• Architecture
  • Hard Parameter Sharing
  • Soft Parameter Sharing
  • Decoder-focused Model
  • Modulation & Adapters
  • Modularity, MoE, Routing & NAS
  • Task Representation
  • Others
• Optimization
  • Loss & Gradient Strategy
  • Task Sampling
  • Adversarial Training
  • Pareto
  • Distillation
• Task Relationship Learning: Grouping, Hierarchy & Cascading
• Theory
• Misc

• Vandenhende, S., Georgoulis, S., Proesmans, M., Dai, D., & Van Gool, L. Multi-Task Learning for Dense Prediction Tasks: A Survey. TPAMI, 2021.
• Crawshaw, M. Multi-Task Learning with Deep Neural Networks: A Survey. ArXiv, 2020.
• Worsham, J., & Kalita, J. Multi-task learning for natural language processing in the 2020s: Where are we going? Pattern Recognition Letters, 2020.
• Gong, T., Lee, T., Stephenson, C., Renduchintala, V., Padhy, S., Ndirango, A., Keskin, G., & Elibol, O. H. A Comparison of Loss Weighting Strategies for Multi task Learning in Deep Neural Networks. IEEE Access, 2019.
• Li, J., Liu, X., Yin, W., Yang, M., Ma, L., & Jin, Y. Empirical Evaluation of Multi-task Learning in Deep Neural Networks for Natural Language Processing. Neural Computing and Applications, 2021.
• Ruder, S. An Overview of Multi-Task Learning in Deep Neural Networks. ArXiv, 2017.
• Zhang, Y., & Yang, Q. A Survey on Multi-Task Learning. IEEE TKDE, 2021.

• MultiMNIST/MultiFashionMNIST
  • a multitask variant of the MNIST/FashionMNIST dataset
  • [ ! ] Toy datasets
  • Reference: MGDA, Pareto MTL, IT-MTL, etc.
• NYUv2 [URL]
  • 3 Tasks: Semantic Segmentation, Depth Estimation, Surface Normal Estimation
  • Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012). Indoor Segmentation and Support Inference from RGBD Images. ECCV, 2012.
• CityScapes [URL]
  • 3 Tasks: Semantic Segmentation, Instance Segmentation, Depth Estimation
• PASCAL Context [URL]
  • Tasks: Semantic Segmentation, Human Part Segmentation, Semantic Edge Detection, Surface Normals Prediction, Saliency Detection.
• CelebA [URL]
  • Tasks: 40 human face Attributes.
• Taskonomy [URL]
  • 26 Tasks: Scene Categorization, Semantic Segmentation, Edge Detection, Monocular Depth Estimation, Keypoint Detection, etc.
  • Zamir, A. R., Sax, A., Shen, W., Guibas, L. J., Malik, J., & Savarese, S. Taskonomy: Disentangling Task Transfer Learning. CVPR, 2018.
• Visual Domain Decathlon [URL]
  • 10 Datasets: ImageNet, Aircraft, CIFAR100, etc.
  • Multi-domain multi-task learning
  • Rebuffi, S.-A., Bilen, H., & Vedaldi, A. Learning multiple visual domains with residual adapters. NeurIPS, 2017.
• BDD100K [URL]
  • 10-task Driving Dataset
  • Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., & Darrell, T. BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning. CVPR, 2020.
• MS COCO
  • Object detection, pose estimation, semantic segmentation.
  • Reference: MultiTask-CenterNet (MCN): Efficient and Diverse Multitask Learning using an Anchor Free Approach.

• GLUE - General Language Understanding Evaluation [URL]

• MetaWorld [URL]
• MTEnv [URL]

• QM9 [URL]
  • 11 properties of molecules; multi-task regression
  • Reference: Multi-Task Learning as a Bargaining Game.

• MovieLens [URL]
  • 2 Task: binary classification (whether the user will watch) & regression (user’s rating)
  • Reference: DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning

• General

  • LibMTL: LibMTL: A PyTorch Library for Multi-Task Learning

• Computer Vision

  • Multi-Task-Learning-PyTorch: PyTorch implementation of multi-task learning architectures
  • mtan: The implementation of "End-to-End Multi-Task Learning with Attention"
  • astmt: Attentive Single-tasking of Multiple Tasks

• NLP

  • mt-dnn: Multi-Task Deep Neural Networks for Natural Language Understanding

• Recommendation System

  • MTReclib: MTReclib provides a PyTorch implementation of multi-task recommendation models and common datasets.

• RL

  • mtrl: Multi Task RL Baselines

• Heuer, F., Mantowsky, S., Bukhari, S. S., & Schneider, G. MultiTask-CenterNet (MCN): Efficient and Diverse Multitask Learning using an Anchor Free Approach. ICCV, 2021.
• Hu, R., & Singh, A. UniT: Multimodal Multitask Learning with a Unified Transformer. Arxiv, 2021.
• Liu, X., He, P., Chen, W., & Gao, J. Multi-Task Deep Neural Networks for Natural Language Understanding. ACL, 2019.
• Kokkinos, I. UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory. CVPR, 2017.
• Teichmann, M., Weber, M., Zoellner, M., Cipolla, R., & Urtasun, R. MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving. Arxiv, 2016.
• Caruana, R. Multitask Learning. 1997.

• Ruder, S., Bingel, J., Augenstein, I., & Søgaard, A. Latent Multi-task Architecture Learning. AAAI, 2019.
• Gao, Y., Ma, J., Zhao, M., Liu, W., & Yuille, A. L. NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction. CVPR, 2019.
• Long, M., Cao, Z., Wang, J., & Yu, P. S. Learning Multiple Tasks with Multilinear Relationship Networks. NeurIPS, 2017.
• Misra, I., Shrivastava, A., Gupta, A., & Hebert, M. Cross-Stitch Networks for Multi-task Learning. CVPR, 2016.
• Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., & Hadsell, R. Progressive Neural Networks. ArXiv, 2016.
• Yang, Y., & Hospedales, T. Deep Multi-task Representation Learning: A Tensor Factorisation Approach. ICLR, 2017.
• Yang, Y., & Hospedales, T. M. Trace Norm Regularised Deep Multi-Task Learning. ICLR Workshop, 2017.

• Bruggemann, D., Kanakis, M., Obukhov, A., Georgoulis, S., & Van Gool, L. Exploring Relational Context for Multi-Task Dense Prediction. ICCV, 2021.
• Vandenhende, S., Georgoulis, S., & Van Gool, L. MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning. ECCV, 2020.
• Zhang, Z., Cui, Z., Xu, C., Yan, Y., Sebe, N., & Yang, J. Pattern-Affinitive Propagation Across Depth, Surface Normal and Semantic Segmentation. CVPR, 2019.
• Xu, D., Ouyang, W., Wang, X., & Sebe, N. PAD-Net: Multi-tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing. CVPR, 2018.

• He, J., Zhou, C., Ma, X., Berg-Kirkpatrick, T., & Neubig, G. Towards a Unified View of Parameter-Efficient Transfer Learning. ArXiv, 2021.
• Zhu, Y., Feng, J., Zhao, C., Wang, M., & Li, L. Counter-Interference Adapter for Multilingual Machine Translation. Findings of EMNLP, 2021.
• Pilault, J., Elhattami, A., & Pal, C. J. Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data. ICLR, 2021.
• Kanakis, M., Bruggemann, D., Saha, S., Georgoulis, S., Obukhov, A., & Van Gool, L. Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference. ECCV, 2020.
• Pham, M. Q., Crego, J. M., Yvon, F., & Senellart, J. A Study of Residual Adapters for Multi-Domain Neural Machine Translation. WMT, 2020.
• Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., & Gurevych, I. AdapterHub: A Framework for Adapting Transformers. EMNLP 2020: Systems Demonstrations.
• Pfeiffer, J., Vulić, I., Gurevych, I., & Ruder, S. MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer. EMNLP, 2020.
• Zhao, M., Lin, T., Mi, F., Jaggi, M., & Schütze, H. Masking as an Efficient Alternative to Finetuning for Pretrained Language Models. EMNLP, 2020.
• Pfeiffer, J., Kamath, A., Rücklé, A., Cho, K., & Gurevych, I. AdapterFusion: Non-Destructive Task Composition for Transfer Learning. EACL, 2021.
• [MTAN] Liu, S., Johns, E., & Davison, A. J. End-to-End Multi-Task Learning with Attention. CVPR, 2019.
• Strezoski, G., Noord, N., & Worring, M. Many Task Learning With Task Routing. ICCV, 2019.
• Maninis, K.-K., Radosavovic, I., & Kokkinos, I. Attentive Single-Tasking of Multiple Tasks. CVPR, 2019.
• Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., de Laroussilhe, Q., Gesmundo, A., Attariyan, M., & Gelly, S. Parameter-Efficient Transfer Learning for NLP. ICML, 2019.
• Stickland, A. C., & Murray, I. BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning. ICML, 2019.
• Zhao, X., Li, H., Shen, X., Liang, X., & Wu, Y. A Modulation Module for Multi-task Learning with Applications in Image Retrieval. ECCV, 2018.
• Rebuffi, S.-A., Vedaldi, A., & Bilen, H. Efficient Parametrization of Multi-domain Deep Neural Networks. CVPR, 2018.
• Rebuffi, S.-A., Bilen, H., & Vedaldi, A. Learning multiple visual domains with residual adapters. NeurIPS, 2017.

• Tang, D., Zhang, F., Dai, Y., Zhou, C., Wu, S., & Shi, S. One Model, Multiple Tasks: Pathways for Natural Language Understanding. Arxiv, 2022.
• Ponti, E. M., Sordoni, A., Bengio, Y., & Reddy, S. Combining Modular Skills in Multitask Learning. Arxiv, 2022.
• Hazimeh, H., Zhao, Z., Chowdhery, A., Sathiamoorthy, M., Chen, Y., Mazumder, R., Hong, L., & Chi, E. H. DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning. NeurIPS, 2021.
• [Pathways] Introducing Pathways: A next-generation AI architecture. Oct 28, 2021. Retrieved March 9, 2022, from https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/
• Yang, R., Xu, H., Wu, Y., & Wang, X. Multi-Task Reinforcement Learning with Soft Modularization. NeurIPS, 2020.
• Sun, X., Panda, R., & Feris, R. AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning. NeurIPS, 2020.
• Bruggemann, D., Kanakis, M., Georgoulis, S., & Van Gool, L. Automated Search for Resource-Efficient Branched Multi-Task Networks. BMVC, 2020.
• Gao, Y., Bai, H., Jie, Z., Ma, J., Jia, K., & Liu, W. MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning. CVPR, 2020.
• [PLE] Tang, H., Liu, J., Zhao, M., & Gong, X. Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations. RecSys, 2020.
• Bragman, F., Tanno, R., Ourselin, S., Alexander, D., & Cardoso, J. Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels. ICCV, 2019.
• Ahn, C., Kim, E., & Oh, S. Deep Elastic Networks with Model Selection for Multi-Task Learning. ICCV, 2019.
• Ma, J., Zhao, Z., Chen, J., Li, A., Hong, L., & Chi, E. H. SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning. AAAI, 2019.
• Maziarz, K., Kokiopoulou, E., Gesmundo, A., Sbaiz, L., Bartok, G., & Berent, J. Flexible Multi-task Networks by Learning Parameter Allocation. ArXiv, 2019.
• Newell, A., Jiang, L., Wang, C., Li, L.-J., & Deng, J. Feature Partitioning for Efficient Multi-Task Architectures. ArXiv, 2019.
• [MMoE] Ma, J., Zhao, Z., Yi, X., Chen, J., Hong, L., & Chi, E. H. Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts. KDD, 2018.
• Rosenbaum, C., Klinger, T., & Riemer, M. Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning. ICLR, 2018.
• Meyerson, E., & Miikkulainen, R. Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering. ICLR, 2018.
• Liang, J., Meyerson, E., & Miikkulainen, R. Evolutionary architecture search for deep multitask networks. Proceedings of the Genetic and Evolutionary Computation Conference, 2018.
• Kim, E., Ahn, C., & Oh, S. NestedNet: Learning Nested Sparse Structures in Deep Neural Networks. CVPR, 2018.
• Andreas, J., Klein, D., & Levine, S. Modular Multitask Reinforcement Learning with Policy Sketches. ICML, 2017.
• Devin, C., Gupta, A., Darrell, T., Abbeel, P., & Levine, S. Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer. ICRA, 2017
• Fernando, C., Banarse, D., Blundell, C., Zwols, Y., Ha, D., Rusu, A. A., Pritzel, A., & Wierstra, D. PathNet: Evolution Channels Gradient Descent in Super Neural Networks. ArXiv, 2017.

• Sodhani, S., Zhang, A., & Pineau, J. Multi-Task Reinforcement Learning with Context-based Representations. ICML, 2021.

• Zamir, A., Sax, A., Yeo, T., Kar, O., Cheerla, N., Suri, R., Cao, Z., Malik, J., & Guibas, L. Robust Learning Through Cross-Task Consistency. CVPR, 2020.
• Sun, T., Shao, Y., Li, X., Liu, P., Yan, H., Qiu, X., & Huang, X. Learning Sparse Sharing Architectures for Multiple Tasks. AAAI, 2020.
• Lee, H. B., Yang, E., & Hwang, S. J. Deep Asymmetric Multi-task Feature Learning. ICML, 2018.
• Zhang, Y., Wei, Y., & Yang, Q. Learning to Multitask. NeurIPS, 2018.
• Mallya, A., Davis, D., & Lazebnik, S. Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights. ECCV 2018.
• Mallya, A., & Lazebnik, S. PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning. CVPR, 2018.
• Lee, G., Yang, E., & Hwang, S. J. Asymmetric Multi-task Learning based on Task Relatedness and Confidence. ICML, 2016.

• [Unitary Scalarization] Kurin, V., De Palma, A., Kostrikov, I., Whiteson, S., & Kumar, M. P. In Defense of the Unitary Scalarization for Deep Multi-Task Learning. ArXiv, 2022.
  • Minimize the multi-task training objective with a standard gradient-based algorithm.
• [Nash-MTL] Navon, A., Shamsian, A., Achituve, I., Maron, H., Kawaguchi, K., Chechik, G., & Fetaya, E. Multi-Task Learning as a Bargaining Game. Arxiv, 2022.
• [CAGrad] Liu, B., Liu, X., Jin, X., Stone, P., & Liu, Q. Conflict-Averse Gradient Descent for Multi-task Learning. NeurIPS, 2021.
• [Rotograd] Javaloy, A., & Valera, I. Rotograd: Dynamic Gradient Homogenization for Multi-Task Learning. ArXiv, 2021.
• [RLW] Lin, B., Ye, F., & Zhang, Y. A Closer Look at Loss Weighting in Multi-Task Learning. ArXiv, 2021.
• [Gradient Vaccine] Wang, Z., Tsvetkov, Y., Firat, O., & Cao, Y. Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models. ICLR, 2021.
• [IMTL] Liu, L., Li, Y., Kuang, Z., Xue, J.-H., Chen, Y., Yang, W., Liao, Q., & Zhang, W. Towards Impartial Multi-task Learning. ICLR, 2021.
• [IT-MTL] Fifty, C., Amid, E., Zhao, Z., Yu, T., Anil, R., & Finn, C. Measuring and Harnessing Transference in Multi-Task Learning. Arxiv, 2020.
• [GradDrop] Chen, Z., Ngiam, J., Huang, Y., Luong, T., Kretzschmar, H., Chai, Y., & Anguelov, D. Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout. NeurIPS, 2020.
• [PCGrad] Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., & Finn, C. Gradient Surgery for Multi-Task Learning. NeurIPS, 2020.
• [Dynamic Stop-and-Go (DSG)] Lu, J., Goswami, V., Rohrbach, M., Parikh, D., & Lee, S. 12-in-1: Multi-Task Vision and Language Representation Learning. CVPR, 2020.
• [Online Learning for Auxiliary losses (OL-AUX)] Lin, X., Baweja, H., Kantor, G., & Held, D. Adaptive Auxiliary Task Weighting for Reinforcement Learning. NeurIPS, 2019.
• [PopArt] Hessel, M., Soyer, H., Espeholt, L., Czarnecki, W., Schmitt, S., & Van Hasselt, H. (2019). Multi-Task Deep Reinforcement Learning with PopArt. AAAI, 2019.
  • PopArt: Learning values across many orders of magnitude. NeurIPS, 2016.
• [Dynamic Weight Average (DWA)] Liu, S., Johns, E., & Davison, A. J. End-to-End Multi-Task Learning with Attention. CVPR, 2019.
• [Geometric Loss Strategy (GLS)] Chennupati, S., Sistu, G., Yogamani, S., & Rawashdeh, S. A. MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learning. CVPR 2019 Workshop on Autonomous Driving (WAD).
• [Orthogonal] Suteu, M., & Guo, Y. Regularizing Deep Multi-Task Networks using Orthogonal Gradients. Arxiv, 2019.
  • Enforcing near orthogonal gradients
• [Cosine Similarity] Du, Y., Czarnecki, W. M., Jayakumar, S. M., Farajtabar, M., Pascanu, R., & Lakshminarayanan, B. Adapting Auxiliary Losses Using Gradient Similarity. Arxiv, 2018.
  • Uses a thresholded cosine similarity to determine whether to use each auxiliary task.
  • Extension: OL-AUX
• [Revised Uncertainty] Liebel, L., & Körner, M. Auxiliary Tasks in Multi-task Learning. Arxiv, 2018.
• [GradNorm] Chen, Z., Badrinarayanan, V., Lee, C.-Y., & Rabinovich, A. GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks. ICML, 2018.
• [Dynamic Task Prioritization] Guo, M., Haque, A., Huang, D.-A., Yeung, S., & Fei-Fei, L. Dynamic Task Prioritization for Multitask Learning. ECCV, 2018.
• [Uncertainty] Kendall, A., Gal, Y., & Cipolla, R. Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics. CVPR, 2018.
• [MGDA] Sener, O., & Koltun, V. Multi-Task Learning as Multi-Objective Optimization. NeurIPS, 2018.
• [AdaLoss] Hu, H., Dey, D., Hebert, M., & Bagnell, J. A. Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing. Arxiv, 2017.
  • The weights are inversely proportional to average of each loss.
• [Task-wise Early Stopping] Zhang, Z., Luo, P., Loy, C. C., & Tang, X. Facial Landmark Detection by Deep Multi-task Learning. ECCV, 2014.

Note:

• We find that AdaLoss, IMTL-l, and Uncertainty are quite similiar in form.

• [MT-Uncertainty Sampling] Pilault, J., Elhattami, A., & Pal, C. J. Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data. ICLR, 2021.
• [Uniform, Task size, Counterfactual] Glover, J., & Hokamp, C. Task Selection Policies for Multitask Learning. ArXiv, 2019.

• Maninis, K.-K., Radosavovic, I., & Kokkinos, I. Attentive Single-Tasking of Multiple Tasks. CVPR, 2019.
• Sinha, A., Chen, Z., Badrinarayanan, V., & Rabinovich, A. Gradient Adversarial Training of Neural Networks. Arxiv, 2018.
• Liu, P., Qiu, X., & Huang, X. Adversarial Multi-task Learning for Text Classification. ACL, 2017.

• Ma, P., Du, T., & Matusik, W. Effcient Continuous Pareto Exploration in Multi-Task Learning. ICML, 2020.
• Lin, X., Zhen, H.-L., Li, Z., Zhang, Q.-F., & Kwong, S. Pareto Multi-Task Learning. NeurIPS, 2019.

• Ghiasi, G., Zoph, B., Cubuk, E. D., Le, Q. V., & Lin, T.-Y. Multi-Task Self-Training for Learning General Representations. ICCV, 2021.
• Li, W. H., & Bilen, H. Knowledge Distillation for Multi-task Learning, ECCV-Workshop, 2020.

• Fifty, C., Amid, E., Zhao, Z., Yu, T., Anil, R., & Finn, C. Efficiently Identifying Task Groupings for Multi-Task Learning. NeurIPS, 2021.
• Vandenhende, S., Georgoulis, S., De Brabandere, B., & Van Gool, L. Branched Multi-Task Networks: Deciding What Layers To Share. BMVC, 2020.
• Standley, T., Zamir, A. R., Chen, D., Guibas, L., Malik, J., & Savarese, S. Which Tasks Should Be Learned Together in Multi-task Learning? ICML, 2020.
• Guo, P., Lee, C.-Y., & Ulbricht, D. Learning to Branch for Multi-Task Learning. ICML, 2020.
• Dwivedi, K., & Roig, G. Representation Similarity Analysis for Efficient Task Taxonomy & Transfer Learning. CVPR, 2019.
• Guo, H., Pasunuru, R., & Bansal, M. AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning. NAACL, 2019.
• Kim, J., Park, Y., Kim, G., & Hwang, S. J. SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization. ICML, 2017.
• Alonso, H. M., & Plank, B. When is multitask learning effective? Semantic sequence prediction under varying data conditions. EACL, 2017.
• Bingel, J., & Søgaard, A. Identifying beneficial task relations for multi-task learning in deep neural networks. EACL, 2017.
• Hand, E. M., & Chellappa, R. Attributes for Improved Attributes: A Multi-Task Network Utilizing Implicit and Explicit Relationships for Facial Attribute Classification. AAAI, 2017.
• Lu, Y., Kumar, A., Zhai, S., Cheng, Y., Javidi, T., & Feris, R. Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification. CVPR, 2017.
• Søgaard, A., & Goldberg, Y. Deep multi-task learning with low level tasks supervised at lower layers. ACL, 2016.
• Kang, Z., Grauman, K., & Sha, F. Learning with Whom to Share in Multi-task Feature Learning. ICML, 2011.
• Kumar, A., & Daume III, H. Learning Task Grouping and Overlap in Multi-task Learning. ICML, 2012.

• Tiomoko, M., Ali, H. T., & Couillet, R. Deciphering and Optimizing Multi-Task Learning: A Random Matrix Approach. ICLR, 2021.
• Tripuraneni, N., Jordan, M. I., & Jin, C. On the Theory of Transfer Learning: The Importance of Task Diversity. NeurIPS, 2020.
• Wu, S., Zhang, H. R., & Re, C. Understanding and Improving Information Transfer in Multi-Task Learning. ICLR, 2020.

• Deng, W., Gould, S., & Zheng, L. What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?. ICML, 2021.
• Lu, J., Goswami, V., Rohrbach, M., Parikh, D., & Lee, S. 12-in-1: Multi-Task Vision and Language Representation Learning. CVPR, 2020.
• Mao, C., Gupta, A., Nitin, V., Ray, B., Song, S., Yang, J., & Vondrick, C. Multitask Learning Strengthens Adversarial Robustness. ECCV, 2020.
• Guo, P., Xu, Y., Lin, B., & Zhang, Y. Multi-Task Adversarial Attack. ArXiv, 2020.
• Clark, K., Luong, M.-T., Khandelwal, U., Manning, C. D., & Le, Q. V. BAM! Born-Again Multi-Task Networks for Natural Language Understanding. ACL, 2019.
• Pramanik, S., Agrawal, P., & Hussain, A. OmniNet: A unified architecture for multi-modal multi-task learning. Arxiv, 2019.
• Zimin, A., & Lampert, C. H. Tasks Without Borders: A New Approach to Online Multi-Task Learning. AMTL Workshop at ICML 2019.
• Meyerson, E., & Miikkulainen, R. Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains. NeurIPS, 2019.
• Meyerson, E., & Miikkulainen, R. Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing---and Back. ICML, 2018.
• Chou, Y.-M., Chan, Y.-M., Lee, J.-H., Chiu, C.-Y., & Chen, C.-S. Unifying and Merging Well-trained Deep Neural Networks for Inference Stage. IJCAI-ECAI, 2018.
• Doersch, C., & Zisserman, A. Multi-task Self-Supervised Visual Learning. ICCV, 2017.
• Smith, V., Chiang, C.-K., Sanjabi, M., & Talwalkar, A. S. Federated Multi-Task Learning. NeurIPS, 2017.
• Kaiser, L., Gomez, A. N., Shazeer, N., Vaswani, A., Parmar, N., Jones, L., & Uszkoreit, J. One Model To Learn Them All. ArXiv, 2017.
• Yang, Y., & Hospedales, T. M. Unifying Multi-Domain Multi-Task Learning: Tensor and Neural Network Perspectives. ArXiv, 2016.
• Yang, Y., & Hospedales, T. M. A Unified Perspective on Multi-Domain and Multi-Task Learning. ICLR, 2015.