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Advances and Challenges in Meta-Learning: A Technical Review (2307.04722v1)

Published 10 Jul 2023 in cs.LG

Abstract: Meta-learning empowers learning systems with the ability to acquire knowledge from multiple tasks, enabling faster adaptation and generalization to new tasks. This review provides a comprehensive technical overview of meta-learning, emphasizing its importance in real-world applications where data may be scarce or expensive to obtain. The paper covers the state-of-the-art meta-learning approaches and explores the relationship between meta-learning and multi-task learning, transfer learning, domain adaptation and generalization, self-supervised learning, personalized federated learning, and continual learning. By highlighting the synergies between these topics and the field of meta-learning, the paper demonstrates how advancements in one area can benefit the field as a whole, while avoiding unnecessary duplication of efforts. Additionally, the paper delves into advanced meta-learning topics such as learning from complex multi-modal task distributions, unsupervised meta-learning, learning to efficiently adapt to data distribution shifts, and continual meta-learning. Lastly, the paper highlights open problems and challenges for future research in the field. By synthesizing the latest research developments, this paper provides a thorough understanding of meta-learning and its potential impact on various machine learning applications. We believe that this technical overview will contribute to the advancement of meta-learning and its practical implications in addressing real-world problems.

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References (191)
  1. “Automl to date and beyond: Challenges and opportunities” In ACM Computing Surveys (CSUR) 54.8 ACM New York, NY, 2021, pp. 1–36
  2. Joaquin Vanschoren “Meta-Learning” In Automated Machine Learning: Methods, Systems, Challenges Springer International Publishing, 2019, pp. 35–61
  3. “Meta-learning in neural networks: A survey” In IEEE transactions on pattern analysis and machine intelligence 44.9 IEEE, 2021, pp. 5149–5169
  4. “A perspective view and survey of meta-learning” In Artificial intelligence review 18 Springer, 2002, pp. 77–95
  5. Mike Huisman, Jan N Van Rijn and Aske Plaat “A survey of deep meta-learning” In Artificial Intelligence Review 54.6 Springer, 2021, pp. 4483–4541
  6. Oriol Vinyals “Talk: Model vs Optimization Meta Learning”, 2017 Neural Information Processing Systems (NIPS’17) URL: https://evolution.ml/pdf/vinyals.pdf
  7. Jake Snell, Kevin Swersky and Richard Zemel “Prototypical networks for few-shot learning” In Advances in neural information processing systems 30, 2017
  8. Chelsea Finn, Pieter Abbeel and Sergey Levine “Model-agnostic meta-learning for fast adaptation of deep networks” In International conference on machine learning, 2017, pp. 1126–1135 PMLR
  9. “Induction Networks for Few-Shot Text Classification” In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019, pp. 3904–3913
  10. “Few-shot aspect category sentiment analysis via meta-learning” In ACM Transactions on Information Systems 41.1 ACM New York, NY, 2023, pp. 1–31
  11. “Meta-learning for low-resource neural machine translation” In 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, 2020, pp. 3622–3631 Association for Computational Linguistics
  12. “Personalizing dialogue agents via meta-learning” In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019, pp. 5454–5459
  13. “Domain Adaptive Dialog Generation via Meta Learning” In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019, pp. 2639–2649
  14. “Meta-learning for low-resource natural language generation in task-oriented dialogue systems” In Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019, pp. 3151–3157
  15. “One-shot visual imitation learning via meta-learning” In Conference on robot learning, 2017, pp. 357–368 PMLR
  16. “ProtoTransformer: A meta-learning approach to providing student feedback” In arXiv preprint arXiv:2107.14035, 2021
  17. “Multi-task learning as multi-objective optimization” In Advances in neural information processing systems 31, 2018
  18. “Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks” In International conference on machine learning, 2018, pp. 794–803 PMLR
  19. Alex Kendall, Yarin Gal and Roberto Cipolla “Multi-task learning using uncertainty to weigh losses for scene geometry and semantics” In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 7482–7491
  20. “Cross-stitch networks for multi-task learning” In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 3994–4003
  21. “Latent multi-task architecture learning” In Proceedings of the AAAI Conference on Artificial Intelligence 33.01, 2019, pp. 4822–4829
  22. “Nddr-cnn: Layerwise feature fusing in multi-task cnns by neural discriminative dimensionality reduction” In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 3205–3214
  23. “Feature-wise transformations” https://distill.pub/2018/feature-wise-transformations In Distill, 2018 DOI: 10.23915/distill.00011
  24. Shikun Liu, Edward Johns and Andrew J Davison “End-to-end multi-task learning with attention” In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 1871–1880
  25. “Learning multiple tasks with multilinear relationship networks” In Advances in neural information processing systems 30, 2017
  26. “Perceiver IO: A General Architecture for Structured Inputs & Outputs” In International Conference on Learning Representations
  27. “Efficiently identifying task groupings for multi-task learning” In Advances in Neural Information Processing Systems 34, 2021, pp. 27503–27516
  28. “A survey on multi-task learning” In IEEE Transactions on Knowledge and Data Engineering 34.12 IEEE, 2021, pp. 5586–5609
  29. Michael Crawshaw “Multi-task learning with deep neural networks: A survey” In arXiv preprint arXiv:2009.09796, 2020
  30. Minyoung Huh, Pulkit Agrawal and Alexei A Efros “What makes ImageNet good for transfer learning?” In arXiv preprint arXiv:1608.08614, 2016
  31. “Bert: Pre-training of deep bidirectional transformers for language understanding” In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers) Association for Computational Linguistics, 2019, pp. 4171–4186
  32. “Palm: Scaling language modeling with pathways” In arXiv preprint arXiv:2204.02311, 2022
  33. “Llama: Open and efficient foundation language models” In arXiv preprint arXiv:2302.13971, 2023
  34. OpenAI “GPT-4 Technical Report” In ArXiv abs/2303.08774, 2023
  35. “Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution” In International Conference on Learning Representations
  36. “Surgical Fine-Tuning Improves Adaptation to Distribution Shifts” In NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications
  37. Jason Phang, Thibault Févry and Samuel R Bowman “Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks” In arXiv preprint arXiv:1811.01088, 2018
  38. “Universal Language Model Fine-tuning for Text Classification” In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018, pp. 328–339
  39. “Meta-learning with memory-augmented neural networks” In International conference on machine learning, 2016, pp. 1842–1850 PMLR
  40. “A simple neural attentive meta-learner” In International Conference on Learning Representations, 2018
  41. “Meta networks” In International conference on machine learning, 2017, pp. 2554–2563 PMLR
  42. “Conditional neural processes” In International conference on machine learning, 2018, pp. 1704–1713 PMLR
  43. “Optimization as a model for few-shot learning” In International conference on learning representations, 2017
  44. “Learning to learn by gradient descent by gradient descent” In Advances in neural information processing systems 29, 2016
  45. “Learning to Optimize” In International Conference on Learning Representations, 2017
  46. “Learned optimizers that scale and generalize” In International Conference on Machine Learning, 2017, pp. 3751–3760 PMLR
  47. “Meta architecture search” In Advances in Neural Information Processing Systems 32, 2019
  48. “Towards fast adaptation of neural architectures with meta learning” In International Conference on Learning Representations, 2019
  49. “Bilevel programming for hyperparameter optimization and meta-learning” In International conference on machine learning, 2018, pp. 1568–1577 PMLR
  50. “Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm” In International Conference on Learning Representations, 2018
  51. “Meta-sgd: Learning to learn quickly for few-shot learning” In arXiv preprint arXiv:1707.09835, 2017
  52. Harkirat Singh Behl, Atılım Güneş Baydin and Philip HS Torr “Alpha maml: Adaptive model-agnostic meta-learning” In 6th ICML Workshop on Automated Machine Learning, Thirty-Sixth International Conference on Machine Learning (ICML), 2019
  53. Fengwei Zhou, Bin Wu and Zhenguo Li “Deep meta-learning: Learning to learn in the concept space” In arXiv preprint arXiv:1802.03596, 2018
  54. “Rapid learning or feature reuse? towards understanding the effectiveness of maml” In International conference on learning representations, 2023
  55. “Boil: Towards representation change for few-shot learning” In The International Conference on Learning Representations (ICLR), 2021
  56. Antreas Antoniou, Harrison Edwards and Amos Storkey “How to train your MAML” In International Conference on Learning Representations, 2018
  57. “Fast context adaptation via meta-learning” In International Conference on Machine Learning, 2019, pp. 7693–7702 PMLR
  58. Markus Hiller, Mehrtash Harandi and Tom Drummond “On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation” In Advances in Neural Information Processing Systems, 2022
  59. Ian Goodfellow, Yoshua Bengio and Aaron Courville “Deep learning” MIT press, 2016
  60. Alex Nichol, Joshua Achiam and John Schulman “On first-order meta-learning algorithms” In arXiv preprint arXiv:1803.02999, 2018
  61. “Meta-learning with differentiable closed-form solvers” In International Conference on Learning Representations (ICLR), 2019, 2019
  62. “Meta-learning with differentiable convex optimization” In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 10657–10665
  63. “Meta-learning with implicit gradients” In Advances in neural information processing systems 32, 2019
  64. “Nearest neighbor pattern classification” In IEEE Transactions on Information Theory 13.1, 1967, pp. 21–27 DOI: 10.1109/TIT.1967.1053964
  65. “The unreasonable effectiveness of deep features as a perceptual metric” In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 586–595
  66. Gregory Koch, Richard Zemel and Ruslan Salakhutdinov “Siamese neural networks for one-shot image recognition” In ICML deep learning workshop 2.1, 2015 Lille
  67. “Matching networks for one shot learning” In Advances in neural information processing systems 29, 2016
  68. “On episodes, prototypical networks, and few-shot learning” In Advances in Neural Information Processing Systems 34, 2021, pp. 24581–24592
  69. “Learning to compare: Relation network for few-shot learning” In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 1199–1208
  70. “Few-shot learning with graph neural networks” In International Conference on Learning Representations, 2018
  71. “Infinite mixture prototypes for few-shot learning” In International Conference on Machine Learning, 2019, pp. 232–241 PMLR
  72. “Learning to learn with conditional class dependencies” In International conference on learning representations, 2019
  73. “Meta-learning with latent embedding optimization” In International Conference on Learning Representations, 2019
  74. “Meta-dataset: A dataset of datasets for learning to learn from few examples” In International Conference on Learning Representations, 2020
  75. “A hybrid approach with optimization-based and metric-based meta-learner for few-shot learning” In Neurocomputing 349 Elsevier, 2019, pp. 202–211
  76. “Rethinking few-shot image classification: a good embedding is all you need?” In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIV 16, 2020, pp. 266–282 Springer
  77. “Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning” In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 9062–9071
  78. “A broader study of cross-domain few-shot learning” In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXVII 16, 2020, pp. 124–141 Springer
  79. “A Closer Look at Few-shot Classification” In International Conference on Learning Representations, 2019
  80. “Multimodal model-agnostic meta-learning via task-aware modulation” In Advances in neural information processing systems 32, 2019
  81. Giulia Denevi, Massimiliano Pontil and Carlo Ciliberto “The advantage of conditional meta-learning for biased regularization and fine tuning” In Advances in Neural Information Processing Systems 33, 2020, pp. 964–974
  82. “Hierarchically structured meta-learning” In International Conference on Machine Learning, 2019, pp. 7045–7054 PMLR
  83. Weisen Jiang, James Kwok and Yu Zhang “Subspace learning for effective meta-learning” In International Conference on Machine Learning, 2022, pp. 10177–10194 PMLR
  84. “Reconciling meta-learning and continual learning with online mixtures of tasks” In Advances in Neural Information Processing Systems 32, 2019
  85. “Task similarity aware meta learning: Theory-inspired improvement on maml” In Uncertainty in Artificial Intelligence, 2021, pp. 23–33 PMLR
  86. “LGM-Net: Learning to generate matching networks for few-shot learning” In International conference on machine learning, 2019, pp. 3825–3834 PMLR
  87. “A Universal Representation Transformer Layer for Few-Shot Image Classification” In International Conference on Learning Representations, 2020
  88. Wei-Hong Li, Xialei Liu and Hakan Bilen “Universal representation learning from multiple domains for few-shot classification” In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 9526–9535
  89. “Fast and flexible multi-task classification using conditional neural adaptive processes” In Advances in Neural Information Processing Systems 32, 2019
  90. Nikita Dvornik, Cordelia Schmid and Julien Mairal “Selecting relevant features from a multi-domain representation for few-shot classification” In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part X 16, 2020, pp. 769–786 Springer
  91. “Learning a universal template for few-shot dataset generalization” In International Conference on Machine Learning, 2021, pp. 10424–10433 PMLR
  92. Wei-Yu Lee, Jheng-Yu Wang and Yu-Chiang Frank Wang “Domain-Agnostic Meta-Learning for Cross-Domain Few-Shot Classification” In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 1715–1719 IEEE
  93. “Transferable Meta Learning Across Domains.” In UAI, 2018, pp. 177–187
  94. “Semi-supervised meta-learning for cross-domain few-shot intent classification” In Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing, 2021, pp. 67–75
  95. “Task-level Self-supervision for Cross-domain Few-shot Learning” In Proceedings of the AAAI Conference on Artificial Intelligence 36.3, 2022, pp. 3215–3223
  96. “Self-supervised meta-learning for few-shot natural language classification tasks” In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
  97. “Communication-efficient learning of deep networks from decentralized data” In Artificial intelligence and statistics, 2017, pp. 1273–1282 PMLR
  98. “Federated optimization in heterogeneous networks” In Proceedings of Machine learning and systems 2, 2020, pp. 429–450
  99. Qinbin Li, Bingsheng He and Dawn Song “Model-contrastive federated learning” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 10713–10722
  100. “Scaffold: Stochastic controlled averaging for federated learning” In International Conference on Machine Learning, 2020, pp. 5132–5143 PMLR
  101. “Federated learning of a mixture of global and local models” In arXiv preprint arXiv:2002.05516, 2020
  102. Canh T Dinh, Nguyen Tran and Josh Nguyen “Personalized federated learning with moreau envelopes” In Advances in Neural Information Processing Systems 33, 2020, pp. 21394–21405
  103. “Ditto: Fair and robust federated learning through personalization” In International Conference on Machine Learning, 2021, pp. 6357–6368 PMLR
  104. Jian Xu, Xinyi Tong and Huang Shao-Lun “Personalized Federated Learning with Feature Alignment and Classifier Collaboration” In International conference on learning representations, 2023
  105. “An efficient framework for clustered federated learning” In Advances in Neural Information Processing Systems 33, 2020, pp. 19586–19597
  106. “Flexible clustered federated learning for client-level data distribution shift” In IEEE Transactions on Parallel and Distributed Systems 33.11 IEEE, 2021, pp. 2661–2674
  107. Felix Sattler, Klaus-Robert Müller and Wojciech Samek “Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints” In IEEE transactions on neural networks and learning systems 32.8 IEEE, 2020, pp. 3710–3722
  108. “Personalized federated learning on non-IID data via group-based meta-learning” In ACM Transactions on Knowledge Discovery from Data 17.4 ACM New York, NY, 2023, pp. 1–20
  109. “Improving federated learning personalization via model agnostic meta learning” In arXiv preprint arXiv:1909.12488, 2019
  110. Alireza Fallah, Aryan Mokhtari and Asuman Ozdaglar “Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach” In Advances in Neural Information Processing Systems 33, 2020, pp. 3557–3568
  111. “Federated meta-learning with fast convergence and efficient communication” In arXiv preprint arXiv:1802.07876, 2018
  112. Mikhail Khodak, Maria-Florina F Balcan and Ameet S Talwalkar “Adaptive gradient-based meta-learning methods” In Advances in Neural Information Processing Systems 32, 2019
  113. “A simple framework for contrastive learning of visual representations” In International conference on machine learning, 2020, pp. 1597–1607 PMLR
  114. “Momentum contrast for unsupervised visual representation learning” In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 9729–9738
  115. “Bootstrap your own latent-a new approach to self-supervised learning” In Advances in neural information processing systems 33, 2020, pp. 21271–21284
  116. Aaron van den Oord, Yazhe Li and Oriol Vinyals “Representation learning with contrastive predictive coding” In arXiv preprint arXiv:1807.03748, 2018
  117. Kyle Hsu, Sergey Levine and Chelsea Finn “Unsupervised learning via meta-learning” In International Conference on Learning Representations, 2019
  118. Jeff Donahue, Philipp Krähenbühl and Trevor Darrell “Adversarial feature learning” In International Conference on Learning Representations, 2017
  119. “Large scale adversarial representation learning” In Advances in neural information processing systems 32, 2019
  120. “Deep clustering for unsupervised learning of visual features” In Proceedings of the European conference on computer vision (ECCV), 2018, pp. 132–149
  121. Siavash Khodadadeh, Ladislau Boloni and Mubarak Shah “Unsupervised meta-learning for few-shot image classification” In Advances in neural information processing systems 32, 2019
  122. “Unsupervised meta-learning through latent-space interpolation in generative models” In International Conference on Learning Representations, 2021
  123. “Autoaugment: Learning augmentation strategies from data” In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 113–123
  124. “R3m: A universal visual representation for robot manipulation” In Conference on Robot Learning, 2023, pp. 892–909 PMLR
  125. Alex Tamkin, Mike Wu and Noah Goodman “Viewmaker networks: Learning views for unsupervised representation learning” In International Conference on Learning Representations, 2020
  126. “Meta-gmvae: Mixture of gaussian vae for unsupervised meta-learning” In International Conference on Learning Representations, 2021
  127. Deqian Kong, Bo Pang and Ying Nian Wu “Unsupervised Meta-Learning via Latent Space Energy-based Model of Symbol Vector Coupling” In Fifth Workshop on Meta-Learning at the Conference on Neural Information Processing Systems, 2021
  128. Diederik P Kingma and Max Welling “Auto-encoding variational bayes” In International Conference on Learning Representations, 2014
  129. “Energy-based models for sparse overcomplete representations” In Journal of Machine Learning Research 4.Dec, 2003, pp. 1235–1260
  130. “The close relationship between contrastive learning and meta-learning” In International Conference on Learning Representations, 2021
  131. Zhanyuan Yang, Jinghua Wang and Yingying Zhu “Few-shot classification with contrastive learning” In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XX, 2022, pp. 293–309 Springer
  132. “Self-Supervised Set Representation Learning for Unsupervised Meta-Learning” In International Conference on Learning Representations, 2023
  133. Huiwon Jang, Hankook Lee and Jinwoo Shin “Unsupervised Meta-learning via Few-shot Pseudo-supervised Contrastive Learning” In International Conference on Learning Representations, 2023
  134. “Language models are few-shot learners” In Advances in neural information processing systems 33, 2020, pp. 1877–1901
  135. Chuhan Wu, Fangzhao Wu and Yongfeng Huang “One teacher is enough? pre-trained language model distillation from multiple teachers” In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 2021, pp. 4408–4413
  136. Trapit Bansal, Rishikesh Jha and Andrew McCallum “Learning to few-shot learn across diverse natural language classification tasks” In Proceedings of the 28th International Conference on Computational Linguistics, 2020, pp. 5108–5123
  137. “Deep domain confusion: Maximizing for domain invariance” In arXiv preprint arXiv:1412.3474, 2014
  138. “Domain-adversarial training of neural networks” In The journal of machine learning research 17.1 JMLR. org, 2016, pp. 2096–2030
  139. “Unpaired image-to-image translation using cycle-consistent adversarial networks” In Proceedings of the IEEE international conference on computer vision, 2017, pp. 2223–2232
  140. “Rl-cyclegan: Reinforcement learning aware simulation-to-real” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 11157–11166
  141. “Avid: Learning multi-stage tasks via pixel-level translation of human videos” In arXiv preprint arXiv:1912.04443, 2019
  142. “Cycada: Cycle-consistent adversarial domain adaptation” In International conference on machine learning, 2018, pp. 1989–1998 Pmlr
  143. “Adversarial multiple source domain adaptation” In Advances in neural information processing systems 31, 2018
  144. “Unsupervised multi-target domain adaptation through knowledge distillation” In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2021, pp. 1339–1347
  145. “Blending-target domain adaptation by adversarial meta-adaptation networks” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 2248–2257
  146. “Unsupervised multi-target domain adaptation: An information theoretic approach” In IEEE Transactions on Image Processing 29 IEEE, 2020, pp. 3993–4002
  147. “Adaptive risk minimization: Learning to adapt to domain shift” In Advances in Neural Information Processing Systems 34, 2021, pp. 23664–23678
  148. “Few-Shot Unsupervised Domain Adaptation via Meta Learning” In 2022 IEEE International Conference on Multimedia and Expo (ICME), 2022, pp. 1–6 IEEE
  149. “Similarity-based meta-learning network with adversarial domain adaptation for cross-domain fault identification” In Knowledge-Based Systems 217 Elsevier, 2021, pp. 106829
  150. Anthony Sicilia, Xingchen Zhao and Seong Jae Hwang “Domain adversarial neural networks for domain generalization: When it works and how to improve” In Machine Learning Springer, 2023, pp. 1–37
  151. “Deep coral: Correlation alignment for deep domain adaptation” In Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III 14, 2016, pp. 443–450 Springer
  152. “mixup: Beyond empirical risk minimization” In International Conference on Learning Representations, 2018
  153. “Manifold mixup: Better representations by interpolating hidden states” In International conference on machine learning, 2019, pp. 6438–6447 PMLR
  154. “Improving out-of-distribution robustness via selective augmentation” In International Conference on Machine Learning, 2022, pp. 25407–25437 PMLR
  155. “Domain generalization via model-agnostic learning of semantic features” In Advances in Neural Information Processing Systems 32, 2019
  156. “Episodic training for domain generalization” In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 1446–1455
  157. “Feature-critic networks for heterogeneous domain generalization” In International Conference on Machine Learning, 2019, pp. 3915–3924 PMLR
  158. “Learning to generalize: Meta-learning for domain generalization” In Proceedings of the AAAI conference on artificial intelligence 32.1, 2018
  159. Yogesh Balaji, Swami Sankaranarayanan and Rama Chellappa “Metareg: Towards domain generalization using meta-regularization” In Advances in neural information processing systems 31, 2018
  160. “Open domain generalization with domain-augmented meta-learning” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 9624–9633
  161. Keyu Chen, Di Zhuang and J Morris Chang “Discriminative adversarial domain generalization with meta-learning based cross-domain validation” In Neurocomputing 467 Elsevier, 2022, pp. 418–426
  162. Gido M Ven, Tinne Tuytelaars and Andreas S Tolias “Three types of incremental learning” In Nature Machine Intelligence 4.12 Nature Publishing Group UK London, 2022, pp. 1185–1197
  163. “Gradient episodic memory for continual learning” In Advances in neural information processing systems 30, 2017
  164. “Efficient lifelong learning with a-gem” In International Conference on Learning Representations, 2019
  165. “icarl: Incremental classifier and representation learning” In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, 2017, pp. 2001–2010
  166. Rahaf Aljundi, Marcus Rohrbach and Tinne Tuytelaars “Selfless sequential learning” In International Conference on Learning Representations, 2019
  167. “Overcoming catastrophic forgetting in neural networks” In Proceedings of the national academy of sciences 114.13 National Acad Sciences, 2017, pp. 3521–3526
  168. “Overcoming catastrophic forgetting with hard attention to the task” In International Conference on Machine Learning, 2018, pp. 4548–4557 PMLR
  169. “Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting” In International Conference on Machine Learning, 2019, pp. 3925–3934 PMLR
  170. “Contextual transformation networks for online continual learning” In International Conference on Learning Representations, 2021
  171. “Progressive neural networks” In arXiv preprint arXiv:1606.04671, 2016
  172. “Learning to continually learn” In 24th European Conference on Artificial Intelligence, 2020
  173. “Learning to learn without forgetting by maximizing transfer and minimizing interference” In International Conference on Learning Representations, 2019
  174. “Meta-learning representations for continual learning” In Advances in neural information processing systems 32, 2019
  175. Gunshi Gupta, Karmesh Yadav and Liam Paull “Look-ahead meta learning for continual learning” In Advances in Neural Information Processing Systems 33, 2020, pp. 11588–11598
  176. “Few-shot learning for dermatological disease diagnosis” In Machine Learning for Healthcare Conference, 2019, pp. 532–552 PMLR
  177. “Cross-modal generalization: Learning in low resource modalities via meta-alignment” In Proceedings of the 29th ACM International Conference on Multimedia, 2021, pp. 2680–2689
  178. “Flamingo: a visual language model for few-shot learning” In Advances in Neural Information Processing Systems 35, 2022, pp. 23716–23736
  179. “A generalist agent” In Transactions on Machine Learning Research, 2022
  180. “Meta-learning for few-shot land cover classification” In Proceedings of the ieee/cvf conference on computer vision and pattern recognition workshops, 2020, pp. 200–201
  181. “One-shot imitation from observing humans via domain-adaptive meta-learning” In Robotics: Science and Systems XIV Robotics: ScienceSystems Foundation, 2018
  182. Cuong Q Nguyen, Constantine Kreatsoulas and Kim M Branson “Meta-learning gnn initializations for low-resource molecular property prediction” In 4th Lifelong Machine Learning Workshop at ICML 2020, 2020
  183. “Few-shot human motion prediction via meta-learning” In Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 432–450
  184. “Meta-album: Multi-domain meta-dataset for few-shot image classification” In Advances in Neural Information Processing Systems 35, 2022, pp. 3232–3247
  185. “NEVIS’22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research” In arXiv preprint arXiv:2211.11747, 2022
  186. “Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning” In Conference on robot learning, 2020, pp. 1094–1100 PMLR
  187. “The visual task adaptation benchmark”, 2019
  188. “Taskonomy: Disentangling task transfer learning” In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 3712–3722
  189. “Value: A multi-task benchmark for video-and-language understanding evaluation” In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1), 2021
  190. “Beyond the imitation game: Quantifying and extrapolating the capabilities of language models” In Transactions on Machine Learning Research, 2023
  191. “Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves” In arXiv preprint arXiv:2009.11243, 2020
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Authors (5)
  1. Anna Vettoruzzo (4 papers)
  2. Mohamed-Rafik Bouguelia (5 papers)
  3. Joaquin Vanschoren (68 papers)
  4. Thorsteinn Rögnvaldsson (16 papers)
  5. KC Santosh (7 papers)
Citations (38)
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