In the paper it´s said: However, if Ci-1 is updated through EMA iteratively along φi(pt ), the final C can store the core normal features representing X. IMO it´s meaning that the core set and the feature adaption is learned iteratively. For me it looks like the core set is once initialized in the DSVDD.__init__() and then adapted to all training samples with self._init_centroid followed by the KMEAN compression. Afterwards, the feature adaption is learned with a fixed core set. Do I miss something?
In the paper it´s said:
However, if Ci-1 is updated through EMA iteratively along φi(pt ), the final C can store the core normal features representing X. IMO it´s meaning that the core set and the feature adaption is learned iteratively. For me it looks like the core set is once initialized in theDSVDD.__init__()and then adapted to all training samples withself._init_centroidfollowed by theKMEANcompression. Afterwards, the feature adaption is learned with a fixed core set. Do I miss something?