Continuous transformation learning of translation invariant representations

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Disentangling by Factorising (2018, background): “Humans are able to learn factors of variation unsupervised (Perry et al., 2010).”
How does the brain solve visual object recognition? (2012, neutral): “Foldiak, 1991; Hurri and Hyvarinen, 2003; Wiskott and Sejnowski, 2002; see Section 4), as well as other potentially important types of unsupervised learning that do not require temporal cues (Karlinsky et al., 2008; Perry et al., 2010).”
Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet (2012, methodology): “Of the 272 layer 1 connections, the number to each group in VisNetL is as shown in Table 4.”
Early Visual Concept Learning with Unsupervised Deep Learning (2016, background): “We propose a deep unsupervised generative approach for disentangled factor learning inspired by neuroscience [2, 3, 24, 15].”