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2025 Book Chapter Leveraging Gradient Information for Out-of-Domain Performance EstimationsKhramtsova, Ekaterina, Baktashmotlagh, Mahsa, Zuccon, Guido, Wang, Xi and Salzmann, Mathieu (2025). Leveraging Gradient Information for Out-of-Domain Performance Estimations. Lecture Notes in Computer Science. (pp. 306-321) Cham: Springer Nature Switzerland. doi: 10.1007/978-3-032-06106-5_18 |
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2025 Book Chapter Spectral Distribution Alignment for Enhanced Generalization in RegressionGuo, Kaiyu, Wang, Zijian, Lovell, Brian C. and Baktashmotlagh, Mahsa (2025). Spectral Distribution Alignment for Enhanced Generalization in Regression. Lecture Notes in Computer Science. (pp. 272-288) Cham: Springer Nature Switzerland. doi: 10.1007/978-3-032-06106-5_16 |
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2020 Book Chapter On minimum discrepancy estimation for deep domain adaptationRahman, Mohammad Mahfujur, Fookes, Clinton, Baktashmotlagh, Mahsa and Sridharan, Sridha (2020). On minimum discrepancy estimation for deep domain adaptation. Domain adaptation for visual understanding. (pp. 81-94) edited by Richa Singh, Mayank Vatsa, Vishal M. Patel and Nalini Ratha. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-030-30671-7_6 |
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2017 Book Chapter Learning Domain Invariant Embeddings by Matching DistributionsBaktashmotlagh, Mahsa, Harandi, Mehrtash and Salzmann, Mathieu (2017). Learning Domain Invariant Embeddings by Matching Distributions. Domain Adaptation in Computer Vision Applications. (pp. 95-114) Cham, Switzerland: Springer. doi: 10.1007/978-3-319-58347-1_5 |
