Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks
While tasks could come with varying the number of instances and classes in realistic settings, the existing meta-learning approaches for few-shot classification assume that number of instances per task and class is fixed. Due to such restriction, they learn to equally utilize the meta-knowledge across all the tasks, even when the number of instances per task and class largely varies. Moreover, they do not consider distributional difference in unseen tasks, on which the meta-knowledge may have less usefulness depending on the task relatedness. To overcome these limitations, we propose a novel meta-learning model that adaptively balances the effect of the meta-learning and task-specific learning within each task. Through the learning of the balancing variables, we can decide whether to obtain a solution by relying on the meta-knowledge or task-specific learning. We formulate this objective into a Bayesian inference framework and tackle it using variational inference. We validate our Bayesian Task-Adaptive Meta-Learning (Bayesian TAML) on two realistic task- and class-imbalanced datasets, on which it significantly outperforms existing meta-learning approaches. Further ablation study confirms the effectiveness of each balancing component and the Bayesian learning framework.
The paper proposes a Bayesian approach for meta learning in settings were the tasks might be OOD or have imbalanced class distribution. The proposed approach has 3 task-specific balancing variables with a prior and an inference network. Using an amortized inference scheme, the model unifies the meta-learning objective loss with the lower bound of probabilistic model marginal likelihood. The paper is well-written and well-motivated. I only have some minor comments and questions: - Can you add the standard errors for the results in Section 5.2 (maybe at least in the Appendix)? - Specifically it would be interesting to see if the results for analyzing the class imbalance variable are statistically significant; specially in light of the recent work on the effects of importance weighting in DL (see “What is the Effect of Importance Weighting in Deep Learning?” by Byrd and Lipton) which essentially question the value of importance weighting for handling class imbalance in various DL settings. - For the experiments in Section 5.1 can you also report the values for the three task-specific balancing variables? (Maybe in the Appendix). Minor: “obtains significantly improves over” -> *significantly improves over* Overall, I found the paper interesting and practically useful, although I believe some additions to the empirical evaluation can improve the impact of the paper.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks
While tasks could come with varying the number of instances and classes in realistic settings, the existing meta-learning approaches for few-shot classification assume that number of instances per task and class is fixed. Due to such restriction, they learn to equally utilize the meta-knowledge across all the tasks, even when the number of instances per task and class largely varies. Moreover, they do not consider distributional difference in unseen tasks, on which the meta-knowledge may have less usefulness depending on the task relatedness. To overcome these limitations, we propose a novel meta-learning model that adaptively balances the effect of the meta-learning and task-specific learning within each task. Through the learning of the balancing variables, we can decide whether to obtain a solution by relying on the meta-knowledge or task-specific learning. We formulate this objective into a Bayesian inference framework and tackle it using variational inference. We validate our Bayesian Task-Adaptive Meta-Learning (Bayesian TAML) on two realistic task- and class-imbalanced datasets, on which it significantly outperforms existing meta-learning approaches. Further ablation study confirms the effectiveness of each balancing component and the Bayesian learning framework.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper proposes a Bayesian approach for meta learning in settings were the tasks might be OOD or have imbalanced class distribution. The proposed approach has 3 task-specific balancing variables with a prior and an inference network. Using an amortized inference scheme, the model unifies the meta-learning objective loss with the lower bound of probabilistic model marginal likelihood. The paper is well-written and well-motivated. I only have some minor comments and questions: - Can you add the standard errors for the results in Section 5.2 (maybe at least in the Appendix)? - Specifically it would be interesting to see if the results for analyzing the class imbalance variable are statistically significant; specially in light of the recent work on the effects of importance weighting in DL (see “What is the Effect of Importance Weighting in Deep Learning?” by Byrd and Lipton) which essentially question the value of importance weighting for handling class imbalance in various DL settings. - For the experiments in Section 5.1 can you also report the values for the three task-specific balancing variables? (Maybe in the Appendix). Minor: “obtains significantly improves over” -> *significantly improves over* Overall, I found the paper interesting and practically useful, although I believe some additions to the empirical evaluation can improve the impact of the paper.
assistant
The paper proposes a Bayesian approach for meta learning in settings were the tasks might be OOD or have imbalanced class distribution. The proposed approach has 3 task-specific balancing variables with a prior and an inference network. Using an amortized inference scheme, the model unifies the meta-learning objective loss with the lower bound of probabilistic model marginal likelihood. The paper is well-written and well-motivated. I only have some minor comments and questions: - Can you add the standard errors for the results in Section 5.2 (maybe at least in the Appendix)? - Specifically it would be interesting to see if the results for analyzing the class imbalance variable are statistically significant; specially in light of the recent work on the effects of importance weighting in DL (see “What is the Effect of Importance Weighting in Deep Learning?” by Byrd and Lipton) which essentially question the value of importance weighting for handling class imbalance in various DL settings. - For the experiments in Section 5.1 can you also report the values for the three task-specific balancing variables? (Maybe in the Appendix). Minor: “obtains significantly improves over” -> *significantly improves over* Overall, I found the paper interesting and practically useful, although I believe some additions to the empirical evaluation can improve the impact of the paper.