Agnostic Domain Generalization

By Fabio Maria Carlucci, P. Russo, Tatiana Tommasi, Barbara Caputo. 2018

16 citations recorded by the source corpus.

The ability to generalize across visual domains is crucial for the robustness of visual recognition systems in the wild. Several works have been dedicated to close the gap between a single labeled source domain and a target domain with transductive access to its data. In this paper we focus on the wider domain generalization task involving multiple sources and seamlessly extending to unsupervised domain adaptation when unlabeled target samples are available at training time. We propose a hybrid architecture that we name ADAGE: it gracefully maps different source data towards an agnostic visual domain through pixel-adaptation based on a novel incremental architecture, and closes the remaining domain gap through feature adaptation. Both the adaptive processes are guided by adversarial learning. Extensive experiments show remarkable improvements compared to the state of the art.

Subfields: Representation & Self-Supervised Learning

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Citations in the corpus

Learning Robust Global Representations by Penalizing Local Predictive Power (2019, background): “More recent DG approaches incorporate adversarial (or similar) techniques (Ghifary et al., 2015; Wang et al., 2016; Motiian et al., 2017b; Li et al., 2018a; Carlucci et al., 2018), or build ensembles of per-domain models that are then fused representations together (Bousmalis et al.”
Learning Robust Representations by Projecting Superficial Statistics Out (2018, background): “…of representations through adversarial (or similar) techniques (Ghifary et al., 2015; Wang et al., 2016; Motiian et al., 2017; Li et al., 2018; Carlucci et al., 2018), or building an ensemble of one-for-each-domain deep models and then fusing representations together (Ding & Fu, 2018; Mancini…”
Domain Agnostic Learning with Disentangled Representations (2019, background): “Recently, domain generalization approaches (Li et al., 2018a; Carlucci et al., 2018b; Li et al., 2018b) have been introduced to adapt from multiple labeled source domains to an unseen target domain.”
A Survey of Unsupervised Deep Domain Adaptation (2018, methodology): “[227] survey multi-source domain adaptation, and since then a number of other methods [28, 88, 95, 157, 184, 192, 256, 259, 281] have been developed for this case.”