Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Expert Training: Task Hardness Aware Meta-Learning for Few-Shot
  Classification   Deep neural networks are highly effective when a large number of labeled
samples are available but fail with few-shot classification tasks. Recently,
meta-learning methods have received much attention, which train a meta-learner
on massive additional tasks to gain the knowledge to instruct the few-shot
classification. Usually, the training tasks are randomly sampled and performed
indiscriminately, often making the meta-learner stuck into a bad local optimum.
Some works in the optimization of deep neural networks have shown that a better
arrangement of training data can make the classifier converge faster and
perform better. Inspired by this idea, we propose an easy-to-hard expert
meta-training strategy to arrange the training tasks properly, where easy tasks
are preferred in the first phase, then, hard tasks are emphasized in the second
phase. A task hardness aware module is designed and integrated into the
training procedure to estimate the hardness of a task based on the
distinguishability of its categories. In addition, we explore multiple hardness
measurements including the semantic relation, the pairwise Euclidean distance,
the Hausdorff distance, and the Hilbert-Schmidt independence criterion.
Experimental results on the miniImageNet and tieredImageNetSketch datasets show
that the meta-learners can obtain better results with our expert training
strategy.

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The categories of this paper are: math
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
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The categories of this paper are: eess
32
The categories of this paper are: atom-ph
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The categories of this paper are: quant-ph
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-bio
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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol