Abstract
Many methods have been proposed for adapting a model trained in a label-rich domain (source) to a label-scarce domain (target). These methods have the assumption that these domains completely have the same the categories. However, if examples in target domain are not given label, we cannot make sure that the domains share the category. A target domain may include examples of categories that the source domain does not have (open set domain adaptation), or some categories can be absent in the target domain (partial domain adaptation). Methods that perform well in this situation are very useful. In this chapter, we briefly summarize non-closed domain adaptation settings in the related work and introduce a method for open set domain adaptation. We define the shared class as the known class and the unshared class as the unknown class. Most existing distribution matching based methods do not work well in the open set situation because unknown target samples should not be matched with the source. In this chapter, we introduce a method which utilizes adversarial training (Saito et al. (Open set domain adaptation by backpropagation. In: Proceedings of the European Conference on Computer Vision (ECCV), 2018)). A classifier is trained to make a boundary between the source and the target samples whereas a generator is trained to make target samples far from the boundary. The key idea of the method is to assign two options to the feature generator: aligning them with source known samples or rejecting them as unknown samples. This approach allows to extract features that separate unknown target samples from known target samples.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bendale, A., Boult, T.E.: Towards open set deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., Erhan, D.: Domain separation networks. In: Conference on Advances in Neural Information Processing Systems (2016)
Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., Krishnan, D.: Unsupervised pixel-level domain adaptation with generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Busto, P.P., Gall, J.: Open set domain adaptation. In: Proceedings of the IEEE International Conference on Computer Vision (2017)
Cao, Z., Ma, L., Long, M., Wang, J.: Partial adversarial domain adaptation. In: Proceedings of the European Conference on Computer Vision (2018)
Chen, Y., Li, W., Sakaridis, C., Dai, D., Van Gool, L.: Domain adaptive faster r-cnn for object detection in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2009)
Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning (2015)
Ge, Z., Demyanov, S., Chen, Z., Garnavi, R.: Generative openmax for multi-class open set classification. In: British Machine Vision Conference (2017)
Ghifary, M., Kleijn, W.B., Zhang, M., Balduzzi, D., Li, W.: Deep reconstruction-classification networks for unsupervised domain adaptation. In: Proceedings of the European Conference on Computer Vision (2016)
Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2012)
Gong, B., Grauman, K., Sha, F.: Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation. In: International Conference on Machine Learning (2013)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Conference on Advances in Neural Information Processing Systems (2014)
Gretton, A., Borgwardt, K.M., Rasch, M., Schölkopf, B., Smola, A.J.: A kernel method for the two-sample-problem. In: Conference on Advances in Neural Information Processing Systems (2007)
Hoffman, J., Wang, D., Yu, F., Darrell, T.: Fcns in the wild: Pixel-level adversarial and constraint-based adaptation (2016). Preprint. arXiv:1612.02649
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning (2015)
Jain, L.P., Scheirer, W.J., Boult, T.E.: Multi-class open set recognition using probability of inclusion. In: Proceedings of the European Conference on Computer Vision (2014)
Kingma, D., Ba, J.: Adam: A method for stochastic optimization (2014). Preprint. arXiv:1412.6980
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Conference on Advances in Neural Information Processing Systems (2012)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Liu, M.Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation networks. In: Conference on Advances in Neural Information Processing Systems (2017)
Long, M., Cao, Y., Wang, J., Jordan, M.I.: Learning transferable features with deep adaptation networks. In: International Conference on Machine Learning (2015)
Long, M., Zhu, H., Wang, J., Jordan, M.I.: Unsupervised domain adaptation with residual transfer networks. In: Conference on Advances in Neural Information Processing Systems (2016)
Long, M., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: International Conference on Machine Learning (2017)
Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579–2605 (2008)
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: Neural Information Processing Systems Workshop on Deep Learning and Unsupervised Feature Learning (2011)
Peng, X., Usman, B., Kaushik, N., Hoffman, J., Wang, D., Saenko, K.: Visda: The visual domain adaptation challenge (2017). Preprint. arXiv:1710.06924
Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Proceedings of the European Conference on Computer Vision (2010)
Saito, K., Ushiku, Y., Harada, T.: Asymmetric tri-training for unsupervised domain adaptation. In: International Conference on Machine Learning (2017)
Saito, K., Watanabe, K., Ushiku, Y., Harada, T.: Maximum classifier discrepancy for unsupervised domain adaptation (2017). Preprint. arXiv:1712.02560
Saito, K., Yamamoto, S., Ushiku, Y., Harada, T.: Open set domain adaptation by backpropagation. In: Proceedings of the European Conference on Computer Vision (2018)
Saito, K., Ushiku, Y., Harada, T., Saenko, K.: Strong-weak distribution alignment for adaptive object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training gans. In: Conference on Advances in Neural Information Processing Systems (2016)
Sener, O., Song, H.O., Saxena, A., Savarese, S.: Learning transferrable representations for unsupervised domain adaptation. In: Conference on Advances in Neural Information Processing Systems (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). Preprint. arXiv:1409.1556
Taigman, Y., Polyak, A., Wolf, L.: Unsupervised cross-domain image generation. In: International Conference on Learning Representations (2016)
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., Darrell, T.: Deep domain confusion: Maximizing for domain invariance (2014). Preprint. arXiv:1412.3474
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Yan, H., Ding, Y., Li, P., Wang, Q., Xu, Y., Zuo, W.: Mind the class weight bias: Weighted maximum mean discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
You, K., Long, M., Cao, Z., Wang, J., Jordan, M.I.: Universal domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)
Zhang, J., Ding, Z., Li, W., Ogunbona, P.: Importance weighted adversarial nets for partial domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Saito, K., Yamamoto, S., Ushiku, Y., Harada, T. (2020). Adversarial Learning Approach for Open Set Domain Adaptation. In: Venkateswara, H., Panchanathan, S. (eds) Domain Adaptation in Computer Vision with Deep Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-45529-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-45529-3_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45528-6
Online ISBN: 978-3-030-45529-3
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)