Uncertainty-Guided Alignment for Unsupervised Domain Adaptation in
Regression
- URL: http://arxiv.org/abs/2401.13721v2
- Date: Fri, 26 Jan 2024 10:59:54 GMT
- Title: Uncertainty-Guided Alignment for Unsupervised Domain Adaptation in
Regression
- Authors: Ismail Nejjar, Gaetan Frusque, Florent Forest, Olga Fink
- Abstract summary: Unsupervised Domain Adaptation for Regression aims to adapt a model from a labeled source domain to an unlabeled target domain for regression tasks.
Recent successful works in UDAR mostly focus on subspace alignment, involving the alignment of a selected subspace within the entire feature space.
We propose an effective method for UDAR by incorporating guidance from uncertainty.
- Score: 5.939858158928473
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Unsupervised Domain Adaptation for Regression (UDAR) aims to adapt a model
from a labeled source domain to an unlabeled target domain for regression
tasks. Recent successful works in UDAR mostly focus on subspace alignment,
involving the alignment of a selected subspace within the entire feature space.
This contrasts with the feature alignment methods used for classification,
which aim at aligning the entire feature space and have proven effective but
are less so in regression settings. Specifically, while classification aims to
identify separate clusters across the entire embedding dimension, regression
induces less structure in the data representation, necessitating additional
guidance for efficient alignment. In this paper, we propose an effective method
for UDAR by incorporating guidance from uncertainty. Our approach serves a dual
purpose: providing a measure of confidence in predictions and acting as a
regularization of the embedding space. Specifically, we leverage the Deep
Evidential Learning framework, which outputs both predictions and uncertainties
for each input sample. We propose aligning the parameters of higher-order
evidential distributions between the source and target domains using
traditional alignment methods at the feature or posterior level. Additionally,
we propose to augment the feature space representation by mixing source samples
with pseudo-labeled target samples based on label similarity. This cross-domain
mixing strategy produces more realistic samples than random mixing and
introduces higher uncertainty, facilitating further alignment. We demonstrate
the effectiveness of our approach on four benchmarks for UDAR, on which we
outperform existing methods.
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