Domain Agnostic Few-Shot Learning For Document Intelligence
- URL: http://arxiv.org/abs/2111.00007v1
- Date: Fri, 29 Oct 2021 03:19:31 GMT
- Title: Domain Agnostic Few-Shot Learning For Document Intelligence
- Authors: Jaya Krishna Mandivarapu, Eric bunch, Glenn fung
- Abstract summary: Few-shot learning aims to generalize to novel classes with only a few samples with class labels.
In this work, we address the problem of few-shot document image classification under domain shift.
- Score: 4.243926243206826
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Few-shot learning aims to generalize to novel classes with only a few samples
with class labels. Research in few-shot learning has borrowed techniques from
transfer learning, metric learning, meta-learning, and Bayesian methods. These
methods also aim to train models from limited training samples, and while
encouraging performance has been achieved, they often fail to generalize to
novel domains. Many of the existing meta-learning methods rely on training data
for which the base classes are sampled from the same domain as the novel
classes used for meta-testing. However, in many applications in the industry,
such as document classification, collecting large samples of data for
meta-learning is infeasible or impossible. While research in the field of the
cross-domain few-shot learning exists, it is mostly limited to computer vision.
To our knowledge, no work yet exists that examines the use of few-shot learning
for classification of semi-structured documents (scans of paper documents)
generated as part of a business workflow (forms, letters, bills, etc.). Here
the domain shift is significant, going from natural images to the
semi-structured documents of interest. In this work, we address the problem of
few-shot document image classification under domain shift. We evaluate our work
by extensive comparisons with existing methods. Experimental results
demonstrate that the proposed method shows consistent improvements on the
few-shot classification performance under domain shift.
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