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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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A family of minimal and renormalizable rectangle exchange maps
A domain exchange map (DEM) is a dynamical system defined on a smooth Jordan domain which is a piecewise translation. We explain how to use cut-and-project sets to construct minimal DEMs. Specializing to the case in which the domain is a square and the cut-and-project set is associated to a Galois lattice, we construct an infinite family of DEMs in which each map is associated to a Pisot–Vijayaraghavan (PV) number. We develop a renormalization scheme for these DEMs. Certain DEMs in the family can be composed to create multistage, renormalizable DEMs.
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- Award ID(s):
- 1939926
- PAR ID:
- 10167544
- Date Published:
- Journal Name:
- Ergodic Theory and Dynamical Systems
- ISSN:
- 0143-3857
- Page Range / eLocation ID:
- 1 to 28
- 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/10.1017/etds.2019.77
