This paper studies the unsupervised cross-domain translation problem by proposing a generative framework, in which the probability distribution of each domain is represented by a generative cooperative network that consists of an energy based model and a latent variable model. The use of generative cooperative network enables maximum likelihood learning of the domain model by MCMC teaching, where the energy-based model seeks to fit the data distribution of domain
and distills its knowledge to the latent variable model via MCMC. Specifically, in the MCMC teaching process, the latent variable model parameterized by an encoder-decoder maps examples from the source domain to the target domain, while the energy-based model further refines the mapped results by Langevin revision such that the revised results match to the examples in the target domain in terms of the statistical properties, which are defined by the learned energy function. For the purpose of building up a correspondence between two unpaired domains, the proposed framework simultaneously learns a pair of cooperative networks with cycle consistency, accounting for a two-way translation between two domains, by alternating MCMC teaching. Experiments show that the proposed framework is useful for unsupervised image-to-image translation and unpaired image sequence translation.
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Contrastive Learning and Cycle Consistency-Based Transductive Transfer Learning for Target Annotation
Annotating automatic target recognition images is
challenging; for example, sometimes there is labeled data in the
source domain but no labeled data in the target domain. Therefore,
it is essential to construct an optimal target domain classifier using
the labeled information of the source domain images. For this
purpose, we propose a transductive transfer learning (TTL) network
consisting of an unpaired domain translation network, a pretrained
source domain classifier, and a gradually constructed target
domain classifier. We delve into the unpaired domain translation
network, which simultaneously optimizes cycle consistency and
modulated noise contrastive losses (MoNCE). Furthermore, the
proposed hybrid CUT module integrated into the TTL network
generates synthetic negative patches by noisy features mixup, and
all the negative patches provide modulated weight into the NCE
loss by considering similarity to the query. Apart from that, this
hybrid CUT network considers query selection by entropy-based
attention to specifying domain variants and invariant regions. The
extensive analysis depicted that the proposed transductive network
can successfully annotate civilian, military vehicles, and ship targets
into the three benchmark ATR datasets. We further demonstrate
the importance of each component of the TTL network through
extensive ablation studies into the DSIAC dataset.
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- Award ID(s):
- 1650474
- PAR ID:
- 10496374
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Aerospace and Electronic Systems
- ISSN:
- 0018-9251
- Page Range / eLocation ID:
- 1 to 21
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TAES.2023.3337768
