• Corpus ID: 235358966

Meta-Learning with Fewer Tasks through Task Interpolation

@article{Yao2021MetaLearningWF,
  title={Meta-Learning with Fewer Tasks through Task Interpolation},
  author={Huaxiu Yao and Linjun Zhang and Chelsea Finn},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.02695}
}
Meta-learning enables algorithms to quickly learn a newly encountered task with just a few labeled examples by transferring previously learned knowledge. However, the bottleneck of current meta-learning algorithms is the requirement of a large number of meta-training tasks, which may not be accessible in real-world scenarios. To address the challenge that available tasks may not densely sample the space of tasks, we propose to augment the task set through interpolation. By meta-learning with… 

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References

SHOWING 1-10 OF 64 REFERENCES

Improving Generalization in Meta-learning via Task Augmentation

Two task augmentation methods are proposed, including MetaMix and Channel Shuffle, which outperform state-of-the-art results by a large margin across many datasets and are compatible with existing meta-learning algorithms.

Task Agnostic Meta-Learning for Few-Shot Learning

An entropy-based approach that meta-learns an unbiased initial model with the largest uncertainty over the output labels by preventing it from over-performing in classification tasks, which outperforms compared meta-learning algorithms in both few-shot classification and reinforcement learning tasks.

Concept Learners for Few-Shot Learning

Developing algorithms that are able to generalize to a novel task given only a few labeled examples represents a fundamental challenge in closing the gap between machine- and human-level performance.

Probabilistic Model-Agnostic Meta-Learning

This paper proposes a probabilistic meta-learning algorithm that can sample models for a new task from a model distribution that is trained via a variational lower bound, and shows how reasoning about ambiguity can also be used for downstream active learning problems.

Regularizing Meta-Learning via Gradient Dropout

This paper introduces a simple yet effective method to alleviate the risk of overfitting for gradient-based meta-learning by randomly dropping the gradient in the inner-loop optimization of each parameter in deep neural networks, such that the augmented gradients improve generalization to new tasks.

Provable Meta-Learning of Linear Representations

This paper provides provably fast, sample-efficient algorithms to address the dual challenges of learning a common set of features from multiple, related tasks and transferring this knowledge to new, unseen tasks, which are central to the general problem of meta-learning.

DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference

DReCA (Decomposing datasets into Reasoning Categories), a simple method for discovering and using latent reasoning categories in a dataset, to form additional high quality tasks to improve the accuracy of meta-learners.

Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace

This work demonstrates that the dimension of this learned subspace reflects the complexity of the task-specific learner's adaptation task, and also that the model is less sensitive to the choice of initial learning rates than previous gradient-based meta-learning methods.

A Simple Neural Attentive Meta-Learner

This work proposes a class of simple and generic meta-learner architectures that use a novel combination of temporal convolutions and soft attention; the former to aggregate information from past experience and the latter to pinpoint specific pieces of information.

Meta-Learning Requires Meta-Augmentation

It is demonstrated that meta-augmentation produces large complementary benefits to recently proposed meta-regularization techniques, and is described as a way to add randomness that discourages the base learner and model from learning trivial solutions that do not generalize to new tasks.
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