Abstract
Domain adaptation aims to transfer the enriched label knowledge from large amounts of source data to unlabeled target data. Recent methods start to solve the class-wise domain adaptation problem by incorporating the soft labels to each target data. Although the soft label strategy could alleviate the negative influence caused by the hard label strategy to some extent, the improper propagation sequence ignoring the labeling difficulties of different target examples will lead to confusing probabilities problem. Moreover, the instability of a single propagation model in dealing with various data may also hinder the performance of target label inference. To address these limitations, we propose a Double Ensemble Soft Transfer Network (DESTN) to jointly optimize the class-wise adaptation and learn the discriminative domain-invariant features with clear soft target labels. Our motivation is to construct a Label Propagation Ensemble (LPE) model by various feature subspaces so as to get robust and clear soft target labels for class-wise domain adaptation. Meanwhile, the other Classifiers Ensemble Framework (CEF) is trained on the labeled source samples and the reliable pseudo-labeled target samples for learning the discriminative features during the iteration. Extensive experiments show that DESTN significantly outperforms several state-of-the-art methods.
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Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F. and Vaughan, J.W.: A theory of learning from different domains. Mach. Learn. 79(1), 151-175 (2009). https://doi.org/10.1007/s10994-009-5152-4
Bermúdez Chacón, R., Salzmann, M., Fua, P.: Domain-adaptive multibranch networks. In: ICLR, No. CONF (2020)
Cai, R., Li, Z., Wei, P., Qiao, J., Zhang, K.: Learning disentangled semantic representation for domain adaptation. In: IJCAI (2019)
Cao, M., Zhou, X., Xu, Y., Pang, Y., Yao, B.: Adversarial domain adaptation with semantic consistency for cross-domain image classification. In: CIKM (2019)
Chen, C., Chen, Z., Jiang, B., Jin, X.: Joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation. In: AAAI (2019)
Chen, C., et al.: Progressive feature alignment for unsupervised domain adaptation. In: CVPR (2019)
Chen, M., Zhao, S., Liu, H., Cai, D.: Adversarial-learned loss for domain adaptation. In: AAAI (2020)
Ding, Z., Li, S., Shao, M., Fu, Y.: Graph adaptive knowledge transfer for unsupervised domain adaptation. In: ECCV, pp. 37–52 (2018)
Goodfellow, I., et al.: Generative adversarial nets. In: NeurIPS (2014)
Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., Smola, A.J.: A kernel method for the two-sample-problem. In: NeurIPS, pp. 513–520 (2007)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Hull, J.J.: A database for handwritten text recognition research. IEEE Trans. Pattern Anal. Mach. Intell. 16(5), 550–554 (2002)
Kang, G., Jiang, L., Yang, Y., Hauptmann, A.G.: Contrastive adaptation network for unsupervised domain adaptation. In: CVPR, pp. 4893–4902 (2019)
Laradji, I.H., Babanezhad, R.: M-adda: Unsupervised domain adaptation with deep metric learning. In: ICML (2018)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Liu, H., Long, M., Wang, J., Jordan, M.: Transferable adversarial training: a general approach to adapting deep classifiers. In: ICML, pp. 4013–4022 (2019)
Long, M., Cao, Y., Wang, J., Jordan, M.I.: Learning transferable features with deep adaptation networks. In: ICML (2015)
Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: NeurIPS, pp. 1640–1650 (2018)
Long, M., Zhu, H., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: ICML, pp. 2208–2217 (2017)
Luo, L., Wang, X., Hu, S., Wang, C., Tang, Y., Chen, L.: Close yet distinctive domain adaptation. In: ICCV (2017)
Luo, Y., Zheng, L., Guan, T., Yu, J., Yang, Y.: Taking a closer look at domain shift: category-level adversaries for semantics consistent domain adaptation. In: CVPR, pp. 2507–2516 (2019)
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: NeurIPS (2011)
Pei, Z., Cao, Z., Long, M., Wang, J.: Multi-adversarial domain adaptation. In: AAAI (2018)
Philip, J., Gharbi, M., Zhou, T., Efros, A.A., Drettakis, G.: Multi-view relighting using a geometry-aware network. TOG 38(4), 1–14 (2019)
Ro, H., Park, Y.J., Byun, J.H., Han, T.D.: Display methods of projection augmented reality based on deep learning pose estimation. In: SIGGRAPH (2019)
Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 213–226. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_16
Soh, J.W., Cho, S., Cho, N.I.: Meta-transfer learning for zero-shot super-resolution. In: CVPR (2020)
Sohn, K., Shang, W., Yu, X., Chandraker, M.: Unsupervised domain adaptation for distance metric learning. In: ICLR (2019)
Sun, X., Nasrabadi, N.M., Tran, T.D.: Supervised deep sparse coding networks for image classification. TIP 29, 405–418 (2019)
Tang, H., Jia, K.: Discriminative adversarial domain adaptation. In: AAAI (2020)
Tseng, H.Y., Lee, H.Y., Huang, J.B., Yang, M.H.: Cross-domain few-shot classification via learned feature-wise transformation. In: ICLR (2020)
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: CVPR, pp. 7167–7176 (2017)
Wang, H., Shen, T., Zhang, W., Duan, L., Mei, T.: Classes matter: a fine-grained adversarial approach to cross-domain semantic segmentation. In: ECCV (2020)
Wen, Y., Zhang, K., Li, Z., Qiao, Yu.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_31
Wu, X., et al.: A unified adversarial learning framework for semi-supervised multi-target domain adaptation. In: DASFAA (2020)
Xie, S., Zheng, Z., Chen, L., Chen, C.: Learning semantic representations for unsupervised domain adaptation. In: ICML, pp. 5419–5428 (2018)
Yao, Y., Zhang, Y., Li, X., Ye, Y.: Heterogeneous domain adaptation via soft transfer network. In: ACM MM, pp. 1578–1586 (2019)
Zhang, W., Ouyang, W., Li, W., Xu, D.: Collaborative and adversarial network for unsupervised domain adaptation. In: CVPR, pp. 3801–3809 (2018)
Zhang, Y., Deng, B., Jia, K., Zhang, L.: Label propagation with augmented anchors: A simple semi-supervised learning baseline for unsupervised domain adaptation. In: ECCV (2020)
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This work was supported by the National Key Research and Development Program of China, No.2018YFB1402600.
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Cao, M., Zhou, X., Lin, L., Yao, B. (2021). Double Ensemble Soft Transfer Network for Unsupervised Domain Adaptation. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12682. Springer, Cham. https://doi.org/10.1007/978-3-030-73197-7_34
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DOI: https://doi.org/10.1007/978-3-030-73197-7_34
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