101 research outputs found
Domain Generalization by Solving Jigsaw Puzzles
Human adaptability relies crucially on the ability to learn and merge
knowledge both from supervised and unsupervised learning: the parents point out
few important concepts, but then the children fill in the gaps on their own.
This is particularly effective, because supervised learning can never be
exhaustive and thus learning autonomously allows to discover invariances and
regularities that help to generalize. In this paper we propose to apply a
similar approach to the task of object recognition across domains: our model
learns the semantic labels in a supervised fashion, and broadens its
understanding of the data by learning from self-supervised signals how to solve
a jigsaw puzzle on the same images. This secondary task helps the network to
learn the concepts of spatial correlation while acting as a regularizer for the
classification task. Multiple experiments on the PACS, VLCS, Office-Home and
digits datasets confirm our intuition and show that this simple method
outperforms previous domain generalization and adaptation solutions. An
ablation study further illustrates the inner workings of our approach.Comment: Accepted at CVPR 2019 (oral
Heterogeneous Domain Generalization via Domain Mixup
One of the main drawbacks of deep Convolutional Neural Networks (DCNN) is
that they lack generalization capability. In this work, we focus on the problem
of heterogeneous domain generalization which aims to improve the generalization
capability across different tasks, which is, how to learn a DCNN model with
multiple domain data such that the trained feature extractor can be generalized
to supporting recognition of novel categories in a novel target domain. To
solve this problem, we propose a novel heterogeneous domain generalization
method by mixing up samples across multiple source domains with two different
sampling strategies. Our experimental results based on the Visual Decathlon
benchmark demonstrates the effectiveness of our proposed method. The code is
released in \url{https://github.com/wyf0912/MIXALL
Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information
Deep learning (DL)-based models have demonstrated good performance in medical
image segmentation. However, the models trained on a known dataset often fail
when performed on an unseen dataset collected from different centers, vendors
and disease populations. In this work, we present a random style transfer
network to tackle the domain generalization problem for multi-vendor and center
cardiac image segmentation. Style transfer is used to generate training data
with a wider distribution/ heterogeneity, namely domain augmentation. As the
target domain could be unknown, we randomly generate a modality vector for the
target modality in the style transfer stage, to simulate the domain shift for
unknown domains. The model can be trained in a semi-supervised manner by
simultaneously optimizing a supervised segmentation and an unsupervised style
translation objective. Besides, the framework incorporates the spatial
information and shape prior of the target by introducing two regularization
terms. We evaluated the proposed framework on 40 subjects from the M\&Ms
challenge2020, and obtained promising performance in the segmentation for data
from unknown vendors and centers.Comment: 11 page
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