Robust Domain Generalisation by Enforcing Distribution Invariance

By Sarah Monazam Erfani, Mahsa Baktash, Masud Moshtaghi, Vinh Nguyen, C. Leckie, James Bailey, Kotagiri Ramamohanarao. 2016

63 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Representation & Self-Supervised Learning

1. Haohan Wang
2. Eric P. Xing
3. Dacheng Tao
4. Tongliang Liu
5. Mingming Gong
6. Tao Qin
7. Sarah Monazam Erfani
8. Ya Li

Citations in the corpus

• Learning Robust Global Representations by Penalizing Local Predictive Power (2019, background): “…generalization (DG) methods (Muandet et al., 2013) (or sometimes known as “zero shot domain adaptation” (Kumagai and Iwata, 2018; Niu et al., 2015; Erfani et al., 2016; Li et al., 2017c)) one possesses domain identifiers for a number of known in-sample domains, and the goal is to generalize to a…”
• Learning Robust Representations by Projecting Superficial Statistics Out (2018, methodology): “…new, but has also been studied extensively: covering a wide spectrum of techniques from kernel methods (Muandet et al., 2013; Niu et al., 2015; Erfani et al., 2016; Li et al., 2017c) to more recent deep learning end-to-end methods, where the methods mostly fall into two categories: reducing…”
• Domain Generalization via Conditional Invariant Representations (2018, methodology): “To handle the distribution changes, many existing domain generalization methods aim to learn domain-invariant representations that have stable distributions across all source domains (Muandet, Balduzzi, and Sch¨olkopf 2013; Erfani et al. 2016; Ghifary et al. 2017).”
• Generalizing to Unseen Domains: A Survey on Domain Generalization (2021, background): “activity recognition [65], chest X-ray recognition [76], [90], and EEG-based seizure detection [88].”
• Towards Out-Of-Distribution Generalization: A Survey (2021, unknown): “…[49] [37], [39], [50], [51], [51], [52], [53], [53], [54], [54], [55], [56], [57], [58], [59], [60] [6], [61], [62], [63], [63], [64], [65], [66], [67], [68], [69], [70](Section 4.1.1) [52], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82] [83], [84], [85], [86], [87], [88],…”
• High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning (2016, background): “However, a major challenge for anomaly detection in such domains is how to cope with noisy, large-scale datasets [3-6].”