The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
Abstract
In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features can change from one dataset to the next. In this paper, we address the problem of visual domain adaptation for transferring object models from one dataset or visual domain to another. We introduce ARC-t, a flexible model for supervised learning of non-linear transformations between domains. Our method is based on a novel theoretical result demonstrating that such transformations can be learned in kernel space. Unlike existing work, our model is not restricted to symmetric transformations, nor to features of the same type and dimensionality, making it applicable to a significantly wider set of adaptation scenarios than previous methods. Furthermore, the method can be applied to categories that were not available during training. We demonstrate the ability of our method to adapt object recognition models under a variety of situations, such as differing imaging conditions, feature types and codebooks.
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Cites
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Self-citationDataset Shift in Machine Learning
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Cited by
Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016) · Journal of Big Data · doi:10.1186/s40537-016-0043-6 · Cited by 6,260
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
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Self-citationDomain randomization for transferring deep neural networks from simulation to the real world
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“The computer vision community has devoted significant study to the problem of adapting vision-based models trained in a source domain to a previously unseen target domain (see, e.g., [9], [14], [15], [19], [23], [25], [51]).”
BackgroundDeep domain confusion Maximizing for domain invariance
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., & Darrell, T. (2014) · arXiv · doi:10.48550/arxiv.1412.3474 · Cited by 2,285
Self-citationDeep visual domain adaptation: A survey
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Metadata
- Authors
- Kulis, B., Saenko, K., & Darrell, T.
- Venue
- CVPR 2011
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 1785-1792
- OpenAlex
- W2090923791
- Cited by
- 688
- References
- 25
- FWCI
- 35.68
- Type
- conference paper
- Language
- en
- Open access
- closed
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