@INPROCEEDINGS {9956407,
author = {G. Perez and S. Maji},
booktitle = {2022 26th International Conference on Pattern Recognition (ICPR)},
title = {Domain Adaptors for Hyperspectral Images},
year = {2022},
volume = {},
issn = {},
pages = {3048-3055},
abstract = {We consider the problem of adapting a network trained on three-channel color images to hyperspectral images with a large number of channels. We propose domain adaptors that map the input to be compatible with a network trained on large-scale color image datasets such as ImageNet, to enable learning on small hyperspectral datasets where training a network from scratch may not be effective. We investigate architectures and strategies for training adaptors and evaluate them on a benchmark consisting of multiple datasets. We find that simple schemes such as linear projection or subset selection are often the most effective, but can lead to a loss in performance in some cases. We also propose a novel multi-view adaptor where of the inputs are combined in an intermediate layer of the network in an order-invariant manner that provides further improvements. We present experiments by varying the number of training examples in the benchmark to characterize the accuracy and computational trade-offs offered by these adaptors.},
keywords = {training;adaptation models;color;computer architecture;benchmark testing;semisupervised learning;transformers},
doi = {10.1109/ICPR56361.2022.9956407},
url = {https://doi.ieeecomputersociety.org/10.1109/ICPR56361.2022.9956407},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month = {aug}
}