[19] Steinar Laenen and Luca Bertinetto. On episodes, prototypical networks, and few-shot learning. NeurIPS, 34:24581-24592, 2021.
[20] Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and F Huang. A tutorial on energy-based learning. Predicting structured data, 1(0), 2006.
[21] Hae Beom Lee, Hayeon Lee, Donghyun Na, Saehoon Kim, Minseop Park, Eunho Yang, and Sung Ju Hwang. Learning to balance: Bayesian meta-learning for imbalanced and out-of-distribution tasks. arXiv preprint arXiv:1905.12917, 2019.
[22] Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin. A simple unified framework for detecting out-of-distribution samples and adversarial attacks. NeurIPS, 31, 2018.
[23] Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, and Jinwoo Shin. Robust inference via generative classifiers for handling noisy labels. In ICML, pages 3763-3772. PMLR, 2019.
[24] Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran, and Stefano Soatto. Meta-learning with differentiable convex optimization. In CVPR, pages 10657-10665, 2019.
[25] Yoonho Lee and Seungjin Choi. Gradient-based meta-learning with learned layerwise metric and subspace. In ICML, pages 2927-2936. PMLR, 2018.
[26] Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li. Meta-sgd: Learning to learn quickly for few-shot learning. arXiv preprint arXiv:1707.09835, 2017.
[27] Shiyu Liang, Yixuan Li, and R Srikant. Enhancing the reliability of out-of-distribution image detection in neural networks. In ICLR, 2018.
[28] Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li. Energy-based out-of-distribution detection. pages 21464-21475, 2020.
[29] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In NeurIPS, 2013.
[30] Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel. A simple neural attentive meta-learner. In ICLR, 2018.
[31] Jingchao Ni, Wei Cheng, Zhengzhang Chen, Takayoshi Asakura, Tomoya Soma, Sho Kato, and Haifeng Chen. Superclass-conditional gaussian mixture model for learning fine-grained embeddings. In ICLR, 2022.
[32] Lukasz P Olech and Mariusz Paradowski. Hierarchical gaussian mixture model with objects attached to terminal and non-terminal dendrogram nodes. In Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015, pages 191-201. Springer, 2016.
[33] Boris Oreshkin, Pau Rodríguez López, and Alexandre Lacoste. Tadam: Task dependent adaptive metric for improved few-shot learning. NeurIPS, 31, 2018.
[34] Alexandre Passos, Piyush Rai, Jacques Wainer, and Hal Daumé III. Flexible modeling of latent task structures in multitask learning. In ICML, pages 1283-1290, 2012.
[35] Sanjay Purushotham, Wilka Carvalho, Tanachat Nilanon, and Yan Liu. Variational recurrent adversarial deep domain adaptation. In ICLR, 2017.
[36] Aniruddh Raghu, Maithra Raghu, Samy Bengio, and Oriol Vinyals. Rapid learning or feature reuse? towards understanding the effectiveness of maml. In ICLR, 2020.
[37] Andrei A Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell. Meta-learning with latent embedding optimization. In ICLR, 2018.
[38] Ketan Rajshekhar Shahapure and Charles Nicholas. Cluster quality analysis using silhouette score. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pages 747-748. IEEE, 2020.