Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot Learning   Few-Shot Learning (FSL) is a challenging task, \emph{i.e.}, how to recognize
novel classes with few examples? Pre-training based methods effectively tackle
the problem by pre-training a feature extractor and then predicting novel
classes via a cosine nearest neighbor classifier with mean-based prototypes.
Nevertheless, due to the data scarcity, the mean-based prototypes are usually
biased. In this paper, we attempt to diminish the prototype bias by regarding
it as a prototype optimization problem. To this end, we propose a novel
meta-learning based prototype optimization framework to rectify prototypes,
\emph{i.e.}, introducing a meta-optimizer to optimize prototypes. Although the
existing meta-optimizers can also be adapted to our framework, they all
overlook a crucial gradient bias issue, \emph{i.e.}, the mean-based gradient
estimation is also biased on sparse data. To address the issue, we regard the
gradient and its flow as meta-knowledge and then propose a novel Neural
Ordinary Differential Equation (ODE)-based meta-optimizer to polish prototypes,
called MetaNODE. In this meta-optimizer, we first view the mean-based
prototypes as initial prototypes, and then model the process of prototype
optimization as continuous-time dynamics specified by a Neural ODE. A gradient
flow inference network is carefully designed to learn to estimate the
continuous gradient flow for prototype dynamics. Finally, the optimal
prototypes can be obtained by solving the Neural ODE. Extensive experiments on
miniImagenet, tieredImagenet, and CUB-200-2011 show the effectiveness of our
method.

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The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat