Adaptive Task Sampling for Meta-Learning

@article{Liu2020AdaptiveTS,
  title={Adaptive Task Sampling for Meta-Learning},
  author={Chenghao Liu and Zhihao Wang and Doyen Sahoo and Yuan Fang and Kun Zhang and Steven C. H. Hoi},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.08735}
}
Meta-learning methods have been extensively studied and applied in computer vision, especially for few-shot classification tasks. The key idea of meta-learning for few-shot classification is to mimic the few-shot situations faced at test time by randomly sampling classes in meta-training data to construct few-shot tasks for episodic training. While a rich line of work focuses solely on how to extract meta-knowledge across tasks, we exploit the complementary problem on how to generate… 

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References

SHOWING 1-10 OF 63 REFERENCES

Meta-Transfer Learning for Few-Shot Learning

A novel few-shot learning method called meta-transfer learning (MTL) which learns to adapt a deep NN for few shot learning tasks and introduces the hard task (HT) meta-batch scheme as an effective learning curriculum for MTL.

Meta-Learning for Semi-Supervised Few-Shot Classification

This work proposes novel extensions of Prototypical Networks that are augmented with the ability to use unlabeled examples when producing prototypes, and confirms that these models can learn to improve their predictions due to unlabeling examples, much like a semi-supervised algorithm would.

Meta-SGD: Learning to Learn Quickly for Few Shot Learning

Meta-SGD, an SGD-like, easily trainable meta-learner that can initialize and adapt any differentiable learner in just one step, shows highly competitive performance for few-shot learning on regression, classification, and reinforcement learning.

On First-Order Meta-Learning Algorithms

A family of algorithms for learning a parameter initialization that can be fine-tuned quickly on a new task, using only first-order derivatives for the meta-learning updates, including Reptile, which works by repeatedly sampling a task, training on it, and moving the initialization towards the trained weights on that task.

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

This work proposes Meta-Dataset: a new benchmark for training and evaluating models that is large-scale, consists of diverse datasets, and presents more realistic tasks, and proposes a new set of baselines for quantifying the benefit of meta-learning in Meta- Dataset.

Meta-Learning With Differentiable Convex Optimization

The objective is to learn feature embeddings that generalize well under a linear classification rule for novel categories and this work exploits two properties of linear classifiers: implicit differentiation of the optimality conditions of the convex problem and the dual formulation of the optimization problem.

TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning

TapNets, neural networks augmented with task-adaptive projection for improved few-shot learning by employing a meta-learning strategy with episode-based training, a network and a set of per-class reference vectors are learned across widely varying tasks.

Optimization as a Model for Few-Shot Learning

Learning to Compare: Relation Network for Few-Shot Learning

A conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each, which is easily extended to zero- shot learning.

Learning to Propagate Labels: Transductive Propagation Network for Few-Shot Learning

This paper proposes Transductive Propagation Network (TPN), a novel meta-learning framework for transductive inference that classifies the entire test set at once to alleviate the low-data problem.
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