Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot
  Learning on Category Graph   A variety of machine learning applications expect to achieve rapid learning
from a limited number of labeled data. However, the success of most current
models is the result of heavy training on big data. Meta-learning addresses
this problem by extracting common knowledge across different tasks that can be
quickly adapted to new tasks. However, they do not fully explore
weakly-supervised information, which is usually free or cheap to collect. In
this paper, we show that weakly-labeled data can significantly improve the
performance of meta-learning on few-shot classification. We propose prototype
propagation network (PPN) trained on few-shot tasks together with data
annotated by coarse-label. Given a category graph of the targeted fine-classes
and some weakly-labeled coarse-classes, PPN learns an attention mechanism which
propagates the prototype of one class to another on the graph, so that the
K-nearest neighbor (KNN) classifier defined on the propagated prototypes
results in high accuracy across different few-shot tasks. The training tasks
are generated by subgraph sampling, and the training objective is obtained by
accumulating the level-wise classification loss on the subgraph. The resulting
graph of prototypes can be continually re-used and updated for new tasks and
classes. We also introduce two practical test/inference settings which differ
according to whether the test task can leverage any weakly-supervised
information as in training. On two benchmarks, PPN significantly outperforms
most recent few-shot learning methods in different settings, even when they are
also allowed to train on weakly-labeled data.

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The categories of this paper are: math-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: physics
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The categories of this paper are: nucl-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: quant-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: math
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org
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The categories of this paper are: hep-ex