Inthis paper,werely onabroader viewofpropercompositelosses and arecent construct from information geometry,sourcefunctions, whose fitting alleviates constraints faced by canonical links.

Yet, their method calledSTORM crucially relies on the knowledge of thesmoothness, aswellaboundonthegradient norms.

Inthis paper,wepropose asimple yet effectivemethod for GFSS that does not use the techniques mentioned above. Also, wetheoretically show that our method perfectly maintains the segmentation performance of the base-class modelovermostofthebaseclasses. Through numerical experiments, we demonstrated the effectiveness of our method. It improved in novel-class segmentation performance in the1-shot scenario by6.1% on the PASCAL-5i dataset,4.7%on

Social survey datasets, for example, are typically mixed because they include variables like age (continuous), demographic group (categorical), and Likert scales (ordinal) measuring how strongly a respondent agrees with certain stated opinions. Continuous variables are encoded as real numbers and sometimes called numeric. We refer to variables that admit a total order (e.g.