439 research outputs found
DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images
The domain adaptation of satellite images has recently gained an increasing
attention to overcome the limited generalization abilities of machine learning
models when segmenting large-scale satellite images. Most of the existing
approaches seek for adapting the model from one domain to another. However,
such single-source and single-target setting prevents the methods from being
scalable solutions, since nowadays multiple source and target domains having
different data distributions are usually available. Besides, the continuous
proliferation of satellite images necessitates the classifiers to adapt to
continuously increasing data. We propose a novel approach, coined DAugNet, for
unsupervised, multi-source, multi-target, and life-long domain adaptation of
satellite images. It consists of a classifier and a data augmentor. The data
augmentor, which is a shallow network, is able to perform style transfer
between multiple satellite images in an unsupervised manner, even when new data
are added over the time. In each training iteration, it provides the classifier
with diversified data, which makes the classifier robust to large data
distribution difference between the domains. Our extensive experiments prove
that DAugNet significantly better generalizes to new geographic locations than
the existing approaches
Dual Generator Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
State-of-the-art methods for image-to-image translation with Generative
Adversarial Networks (GANs) can learn a mapping from one domain to another
domain using unpaired image data. However, these methods require the training
of one specific model for every pair of image domains, which limits the
scalability in dealing with more than two image domains. In addition, the
training stage of these methods has the common problem of model collapse that
degrades the quality of the generated images. To tackle these issues, we
propose a Dual Generator Generative Adversarial Network (GGAN), which is a
robust and scalable approach allowing to perform unpaired image-to-image
translation for multiple domains using only dual generators within a single
model. Moreover, we explore different optimization losses for better training
of GGAN, and thus make unpaired image-to-image translation with higher
consistency and better stability. Extensive experiments on six publicly
available datasets with different scenarios, i.e., architectural buildings,
seasons, landscape and human faces, demonstrate that the proposed GGAN
achieves superior model capacity and better generation performance comparing
with existing image-to-image translation GAN models.Comment: 16 pages, 7 figures, accepted to ACCV 201
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