Ranking Distance Calibration for Cross-Domain Few-Shot Learning
- URL: http://arxiv.org/abs/2112.00260v1
- Date: Wed, 1 Dec 2021 03:36:58 GMT
- Title: Ranking Distance Calibration for Cross-Domain Few-Shot Learning
- Authors: Pan Li, Shaogang Gong, Yanwei Fu, Chengjie Wang
- Abstract summary: Recent progress in few-shot learning promotes a more realistic cross-domain setting.
Due to the domain gap and disjoint label spaces between source and target datasets, their shared knowledge is extremely limited.
We employ a re-ranking process for calibrating a target distance matrix by discovering the reciprocal k-nearest neighbours within the task.
- Score: 91.22458739205766
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent progress in few-shot learning promotes a more realistic cross-domain
setting, where the source and target datasets are from different domains. Due
to the domain gap and disjoint label spaces between source and target datasets,
their shared knowledge is extremely limited. This encourages us to explore more
information in the target domain rather than to overly elaborate training
strategies on the source domain as in many existing methods. Hence, we start
from a generic representation pre-trained by a cross-entropy loss and a
conventional distance-based classifier, along with an image retrieval view, to
employ a re-ranking process for calibrating a target distance matrix by
discovering the reciprocal k-nearest neighbours within the task. Assuming the
pre-trained representation is biased towards the source, we construct a
non-linear subspace to minimise task-irrelevant features therewithin while keep
more transferrable discriminative information by a hyperbolic tangent
transformation. The calibrated distance in this target-aware non-linear
subspace is complementary to that in the pre-trained representation. To impose
such distance calibration information onto the pre-trained representation, a
Kullback-Leibler divergence loss is employed to gradually guide the model
towards the calibrated distance-based distribution. Extensive evaluations on
eight target domains show that this target ranking calibration process can
improve conventional distance-based classifiers in few-shot learning.
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