Abstract: Conventional CNN requires a finite number of pre-known classes with many examples. We present here an associative computing approach and chip architecture for Few Shot training networks using O (1) similarity search and Top K computing. This approach enables only few examples per label, extreme classification and fast adjustment.
Avidan Akerib, PhD
VP, Associative Computing at GSI Technology