Meta-learning approaches enable machine learning systems to adapt to new
tasks given few examples by leveraging knowledge from related tasks. However, a
large number of meta-training tasks are still required for generalization to
unseen tasks during meta-testing, which introduces a critical bottleneck for
real-world problems that come with only few tasks, due to various reasons
including the difficulty and cost of constructing tasks. Recently, several task
augmentation methods have been proposed to tackle this issue using
domain-specific knowledge to design augmentation techniques to densify the
meta-training task distribution. However, such reliance on domain-specific
knowledge renders these methods inapplicable to other domains. While Manifold
Mixup based task augmentation methods are domain-agnostic, we empirically find
them ineffective on non-image domains. To tackle these limitations, we propose
a novel domain-agnostic task augmentation method, Meta-Interpolation, which
utilizes expressive neural set functions to densify the meta-training task
distribution using bilevel optimization. We empirically validate the efficacy
of Meta-Interpolation on eight datasets spanning across various domains such as
image classification, molecule property prediction, text classification and
speech recognition. Experimentally, we show that Meta-Interpolation
consistently outperforms all the relevant baselines. Theoretically, we prove
that task interpolation with the set function regularizes the meta-learner to
improve generalization.

提出了一种利用神经集函数和双层优化，通过任务插值来加强元训练任务分布，以提高泛化性能的新颖领域不可知任务增强方法 Meta-Interpolation，并在跨越图像分类、分子属性预测、文本分类和语音识别等多个领域的实验中进行了验证，结果表明 Meta-Interpolation 始终优于相关基线方法。

基于集合的少任务元学习元插值

Set-based Meta-Interpolation for Few-Task Meta-Learning

Few-shot classification aims to recognize unseen classes with few labeled
samples from each class. Many meta-learning models for few-shot classification
elaborately design various task-shared inductive bias (meta-knowledge) to solve
such tasks, and achieve impressive performance. However, when there exists the
domain shift between the training tasks and the test tasks, the obtained
inductive bias fails to generalize across domains, which degrades the
performance of the meta-learning models. In this work, we aim to improve the
robustness of the inductive bias through task augmentation. Concretely, we
consider the worst-case problem around the source task distribution, and
propose the adversarial task augmentation method which can generate the
inductive bias-adaptive 'challenging' tasks. Our method can be used as a simple
plug-and-play module for various meta-learning models, and improve their
cross-domain generalization capability. We conduct extensive experiments under
the cross-domain setting, using nine few-shot classification datasets:
mini-ImageNet, CUB, Cars, Places, Plantae, CropDiseases, EuroSAT, ISIC and
ChestX. Experimental results show that our method can effectively improve the
few-shot classification performance of the meta-learning models under domain
shift, and outperforms the existing works. Our code is available at
this https URL

针对 few-shot 分类在训练和测试分布之间的域变化导致在测试上性能下降的问题，提出了通过任务增强来改善归纳偏置的鲁棒性，具体来说，采用对抗任务增强方法来生成具有挑战性的任务，可以提供简单的即插即用模块来提高元学习模型在跨域通用性中的性能。

通过对抗任务增强的跨领域少样本分类

Cross-Domain Few-Shot Classification via Adversarial Task Augmentation

Meta-learning has proven to be a powerful paradigm for transferring the
knowledge from previous tasks to facilitate the learning of a novel task.
Current dominant algorithms train a well-generalized model initialization which
is adapted to each task via the support set. The crux lies in optimizing the
generalization capability of the initialization, which is measured by the
performance of the adapted model on the query set of each task. Unfortunately,
this generalization measure, evidenced by empirical results, pushes the
initialization to overfit the meta-training tasks, which significantly impairs
the generalization and adaptation to novel tasks. To address this issue, we
actively augment a meta-training task with "more data" when evaluating the
generalization. Concretely, we propose two task augmentation methods, including
MetaMix and Channel Shuffle. MetaMix linearly combines features and labels of
samples from both the support and query sets. For each class of samples,
Channel Shuffle randomly replaces a subset of their channels with the
corresponding ones from a different class. Theoretical studies show how task
augmentation improves the generalization of meta-learning. Moreover, both
MetaMix and Channel Shuffle outperform state-of-the-art results by a large
margin across many datasets and are compatible with existing meta-learning
algorithms.

该研究提出了 MetaMix 和 Channel Shuffle 等两种元学习任务增广方法，以提高元学习的泛化能力。实验结果显示这两种方法都能显著提高元学习在多个数据集上的表现，并且与现有元学习算法兼容。