Domain Adaption in One-Shot Learning

By Nanqing Dong, Eric P. Xing. 2018

49 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Representation & Self-Supervised Learning, Meta & Continual Learning

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

In Representation & Self-Supervised Learning

In Meta & Continual Learning

Citations in the corpus

A Closer Look at Few-shot Classification (2019, methodology): “(2017); Finn et al. (2017); Ravi & Larochelle (2017); Sung et al. (2018); Garcia & Bruna (2018); Qi et al. (2018). One promising direction to few-shot classification is the meta-learning paradigm where transferable knowledge is extracted and propagated from a collection of tasks to prevent overfitti”
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning (2019, background): “2019; Das and Lee 2020), domain adaptation (Dong and Xing 2018; Kang et al. 2018),”
Class-Incremental Domain Adaptation (2020, background): “However, [8] assumes non-overlapping label sets (Cs ∩ Ct = φ), and does not consider the effect of negative transfer during model upgrade.”
Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions (2018, neutral): “, sampled from different distributions [8, 14].”
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation (2020, methodology): “One exception is the work by Dong and Xing (Dong & Xing, 2018) that address the domain shift issue in the one-shot learning setting.”
A Broader Study of Cross-Domain Few-Shot Learning (2019, methodology): “the condition of cross-domain few-shot learning have been previously explored, including feaure-wise transform (FWT) [58], and Adversarial Domain Adaptation with Reinforced Sample (ADA-RSS) Selection [11]. Since the problem setting of ADA-RSS requires the existence of unlabelled data in the target d”
Few-Shot Semantic Segmentation with Prototype Learning (2018, background): “Few-shot learning has been investigated in many computer vision tasks such as image recognition [33, 36] and domain adaption [9, 23].”