Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Few-shot classification refers to learning a classifier for new classes given only a few examples. While a plethora of models have emerged to tackle it, we find the procedure and datasets that are used to assess their progress lacking. To address this limitation, we propose Meta-Dataset: a new benchmark for training and evaluating models that is large-scale, consists of diverse datasets, and presents more realistic tasks. We experiment with popular baselines and meta-learners on Meta-Dataset, along with a competitive method that we propose. We analyze performance as a function of various characteristics of test tasks and examine the models’ ability to leverage diverse training sources for improving their generalization. We also propose a new set of baselines for quantifying the benefit of meta-learning in Meta-Dataset. Our extensive experimentation has uncovered important research challenges and we hope to inspire work in these directions.
This paper proposed a really interesting direction for few-shot and meta-learning, the concept of a *meta-dataset*, which can be used for more realistic evaluation of algorithms. The main contributions are: 1) A more realistic environment for training and testing few-shot learners. 2) Experimental evaluation of popular models 3) Analyses of whether different models benefit from more data, 4) A new meta-learner I think this work is an interesting empirical paper which should be supported by solid experimental results. My concern about this paper in its current form is that the layout/structure of the paper needs to be improved, for example: Considering putting some of the key results in the appendix section in the main text Removal of repeating results from the main text by shortening the early sections
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Few-shot classification refers to learning a classifier for new classes given only a few examples. While a plethora of models have emerged to tackle it, we find the procedure and datasets that are used to assess their progress lacking. To address this limitation, we propose Meta-Dataset: a new benchmark for training and evaluating models that is large-scale, consists of diverse datasets, and presents more realistic tasks. We experiment with popular baselines and meta-learners on Meta-Dataset, along with a competitive method that we propose. We analyze performance as a function of various characteristics of test tasks and examine the models’ ability to leverage diverse training sources for improving their generalization. We also propose a new set of baselines for quantifying the benefit of meta-learning in Meta-Dataset. Our extensive experimentation has uncovered important research challenges and we hope to inspire work in these directions.
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>



This paper proposed a really interesting direction for few-shot and meta-learning, the concept of a *meta-dataset*, which can be used for more realistic evaluation of algorithms. The main contributions are: 1) A more realistic environment for training and testing few-shot learners. 2) Experimental evaluation of popular models 3) Analyses of whether different models benefit from more data, 4) A new meta-learner I think this work is an interesting empirical paper which should be supported by solid experimental results. My concern about this paper in its current form is that the layout/structure of the paper needs to be improved, for example: Considering putting some of the key results in the appendix section in the main text Removal of repeating results from the main text by shortening the early sections
assistant
This paper proposed a really interesting direction for few-shot and meta-learning, the concept of a *meta-dataset*, which can be used for more realistic evaluation of algorithms. The main contributions are: 1) A more realistic environment for training and testing few-shot learners. 2) Experimental evaluation of popular models 3) Analyses of whether different models benefit from more data, 4) A new meta-learner I think this work is an interesting empirical paper which should be supported by solid experimental results. My concern about this paper in its current form is that the layout/structure of the paper needs to be improved, for example: Considering putting some of the key results in the appendix section in the main text Removal of repeating results from the main text by shortening the early sections