Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Field of Experts Prior for Adapting Neural Networks at Test Time   Performance of convolutional neural networks (CNNs) in image analysis tasks
is often marred in the presence of acquisition-related distribution shifts
between training and test images. Recently, it has been proposed to tackle this
problem by fine-tuning trained CNNs for each test image. Such
test-time-adaptation (TTA) is a promising and practical strategy for improving
robustness to distribution shifts as it requires neither data sharing between
institutions nor annotating additional data. Previous TTA methods use a helper
model to increase similarity between outputs and/or features extracted from a
test image with those of the training images. Such helpers, which are typically
modeled using CNNs, can be task-specific and themselves vulnerable to
distribution shifts in their inputs. To overcome these problems, we propose to
carry out TTA by matching the feature distributions of test and training
images, as modelled by a field-of-experts (FoE) prior. FoEs model complicated
probability distributions as products of many simpler expert distributions. We
use 1D marginal distributions of a trained task CNN's features as experts in
the FoE model. Further, we compute principal components of patches of the task
CNN's features, and consider the distributions of PCA loadings as additional
experts. We validate the method on 5 MRI segmentation tasks (healthy tissues in
4 anatomical regions and lesions in 1 one anatomy), using data from 17 clinics,
and on a MRI registration task, using data from 3 clinics. We find that the
proposed FoE-based TTA is generically applicable in multiple tasks, and
outperforms all previous TTA methods for lesion segmentation. For healthy
tissue segmentation, the proposed method outperforms other task-agnostic
methods, but a previous TTA method which is specifically designed for
segmentation performs the best for most of the tested datasets. Our code is
publicly available.

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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: nlin
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The categories of this paper are: plasm-ph
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The categories of this paper are: adap-org
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The categories of this paper are: cmp-lg
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The categories of this paper are: gr-qc
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The categories of this paper are: patt-sol
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The categories of this paper are: q-bio
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex
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The categories of this paper are: ao-sci