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capsule_theory [2018/11/19 23:31]
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capsule_theory [2018/11/19 23:31]
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 We introduce, (1) a novel routing weight initialization technique, (2) an improved CapsNet design that exploits semantic relationships between the primary capsule activations using a densely connected Conditional Random Field and (3) a Cholesky transformation based correlation module to learn a general priority scheme. Our proposed design allows CapsNet to scale better to more complex problems, such as the multi-label classification task, where semantically related categories co-exist with various interdependencies. ​ We introduce, (1) a novel routing weight initialization technique, (2) an improved CapsNet design that exploits semantic relationships between the primary capsule activations using a densely connected Conditional Random Field and (3) a Cholesky transformation based correlation module to learn a general priority scheme. Our proposed design allows CapsNet to scale better to more complex problems, such as the multi-label classification task, where semantically related categories co-exist with various interdependencies. ​
  
-https://​arxiv.org/​abs/​1811.06969v1+https://​arxiv.org/​abs/​1811.06969v1 ​DARCCC: Detecting Adversaries by Reconstruction from Class Conditional CapsulesĀ