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CAFA: Class-Aware Feature Alignment for Test-Time Adaptation | | Sanghun Jung
; Jungsoo Lee
; Nanhee Kim
; Jaegul Choo
; | | Date: |
1 Jun 2022 | | Abstract: | Despite recent advancements in deep learning, deep networks still suffer from
performance degradation when they face new and different data from their
training distributions. Addressing such a problem, test-time adaptation (TTA)
aims to adapt a model to unlabeled test data on test time while making
predictions simultaneously. TTA applies to pretrained networks without
modifying their training procedures, which enables to utilize the already
well-formed source distribution for adaptation. One possible approach is to
align the representation space of test samples to the source distribution
( extit{i.e.,} feature alignment). However, performing feature alignments in
TTA is especially challenging in that the access to labeled source data is
restricted during adaptation. That is, a model does not have a chance to learn
test data in a class-discriminative manner, which was feasible in other
adaptation tasks ( extit{e.g.,} unsupervised domain adaptation) via supervised
loss on the source data. Based on such an observation, this paper proposes
emph{a simple yet effective} feature alignment loss, termed as Class-Aware
Feature Alignment (CAFA), which 1) encourages a model to learn target
representations in a class-discriminative manner and 2) effectively mitigates
the distribution shifts in test time, simultaneously. Our method does not
require any hyper-parameters or additional losses, which are required in the
previous approaches. We conduct extensive experiments and show our proposed
method consistently outperforms existing baselines. | | Source: | arXiv, 2206.00205 | | Services: | Forum | Review | PDF | Favorites | |
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