In this paper, referring to SimCLR, we build a two-staged contrastive learning based network (TCNet), which can not only make full use of the unlabeled PolSAR data, but also extract the classification features of supervised patch samples, and even encode the categorical contrastive information bet...
sensitive to input errors than serial robots. However, this comparison is too limited to draw any general conclusions. Besides, it is virtually impossible to make a meaningful comparison between other pairs of serial and parallel robot. Therefore, there is no simple answer to this question of ...