  Mutual knowledge distillation (MKD) improves a model by distilling knowledge
from another model. However, \textit{not all knowledge is certain and correct},
especially under adverse conditions. For example, label noise usually leads to
less reliable models due to undesired memorization
\cite{zhang2017understanding,arpit2017closer}. Wrong knowledge misleads the
learning rather than helps. This problem can be handled by two aspects: (i)
improving the reliability of a model where the knowledge is from (i.e.,
knowledge source's reliability); (ii) selecting reliable knowledge for
distillation. In the literature, making a model more reliable is widely studied
while selective MKD receives little attention. Therefore, we focus on studying
selective MKD. Concretely, a generic MKD framework, \underline{C}onfident
knowledge selection followed by \underline{M}utual \underline{D}istillation
(CMD), is designed. The key component of CMD is a generic knowledge selection
formulation, making the selection threshold either static (CMD-S) or
progressive (CMD-P). Additionally, CMD covers two special cases: zero-knowledge
and all knowledge, leading to a unified MKD framework. Extensive experiments
are present to demonstrate the effectiveness of CMD and thoroughly justify the
design of CMD. For example, CMD-P obtains new state-of-the-art results in
robustness against label noise.
