Classification-reconstruction learning for open-set recognition

Open-set classification is a problem of handling `unknown' classes that are not contained in the training dataset, whereas traditional classifiers assume that only known classes appear in the test environment. Existing open-set classifiers rely on deep networks trained in a supervised manner on known classes in the training set; this causes specialization of learned representations to known classes and makes it hard to distinguish unknowns from knowns. In contrast, we train networks for joint classification and reconstruction of input data. This enhances the learned representation so as to preserve information useful for separating unknowns from knowns, as well as to discriminate classes of knowns. Our novel Classification-Reconstruction learning for Open-Set Recognition (CROSR) utilizes latent representations for reconstruction and enables robust unknown detection without harming the known-class classification accuracy. Extensive experiments reveal that the proposed method outperforms existing deep open-set classifiers in multiple standard datasets and is robust to diverse outliers. The code is available in https://nae-lab.org/~rei/research/crosr/.Comment: 11 pages, 7 figure

The selective mono and difunctionalization of carbocyclic cleft molecules with pyridyl groups and X-ray crystallographic analysis

This article was published in the journal Tetrahedron [© Elsevier Ltd]. The definitive version is available at: http://dx.doi.org/10.1016/j.tet.2010.10.027The diesterification and selective mono and dialkylation of carbocyclic analogues of Tröger’s base with pyridyl groups has been achieved in high yield and good selectivity giving access to a novel range of cleft molecules capable of binding events. Reaction conditions for the selective functionalization of this carbocyclic cleft molecule are discussed as well as the solid state structures of these newly synthesized ligands