Domain Adaptive Dictionary Learning

By Qiang Qiu, Vishal M. Patel, Pavan K. Turaga, Ramalingam Chellappa. 2012

152 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computational Mechanics

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Citations in the corpus

Domain Adaptation for Visual Applications: A Comprehensive Survey (2017, background): “odalities (textual, visual, audio), such as in the case of parallel text corpora in different languages [97, 98], photos of the same person taken across different poses, illuminations and expressions [27, 29, 99, 100]. Multi-view learning assumes that at training time for the same data instance mult”
Transfer Joint Matching for Unsupervised Domain Adaptation (2014, background): “…TJM aims to reduce the domain difference by jointly matching the features and reweighting the instances across domains in a principled dimensionality reduction procedure, and construct new feature representation that is invariant to both the distribution difference and the irrelevant instances.”
Zero-shot Domain Adaptation without Domain Semantic Descriptors (2018, methodology): “Existing methods infer such models by using additional semantic descriptors characterizing domains such as time information [19, 17, 18], device and location information [42], and pose and illumination information [29].”
Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding (2015, background): “Some more recently effort on the topic of multiview feature em50 bedding including [22, 23, 24, 25, 26].”
Domain adaptive sparse representation-based classification (2015, methodology): “In [15], a function learning framework was presented for the task of transforming a dictionary learned from one visual domain to the other, while maintaining a domain-invariant representation of a signal.”
Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation (2013, background): “Also related is the recent work presented in [27], which jointly learns aligned dictionaries from multiple domains with correspondence available in those domains.”
Frustratingly Easy NBNN Domain Adaptation (2013, background): “As pointed out in [20, 19], a visual word dictionary built on the source set may be a bad reference for local descriptor extracted from the target.”
A Unified Perspective on Multi-Domain and Multi-Task Learning (2014, background): “Despite the majority of research with the categorical assumption on domains, it has recently been generalised by studies considering domains with a (single) continuous parameter such as time (Hoff-2 man et al., 2014) or viewing angle (Qiu et al., 2012).”
Domain Adaptation on the Statistical Manifold (2014, background): “Alternatively, metric learning [30], transformation learning [25] and dictionary learning [29] have been employed.”
Transfer Feature Learning with Joint Distribution Adaptation (2013, background): “statistical property [17, 11], geometric structure [19, 6].”
DASH-N: Joint Hierarchical Domain Adaptation and Feature Learning (2015, methodology): “Sparse representation and dictionary-based methods for domain adaptation [22]–[24] are also gaining a lot of traction .”
Generalized Domain-Adaptive Dictionaries (2013, methodology): “[19] proposed a method for adapting dictionaries for smoothly varying domains using regression.”