  This work investigates the compatibility between label smoothing (LS) and
knowledge distillation (KD). Contemporary findings addressing this thesis
statement take dichotomous standpoints: Muller et al. (2019) and Shen et al.
(2021b). Critically, there is no effort to understand and resolve these
contradictory findings, leaving the primal question -- to smooth or not to
smooth a teacher network? -- unanswered. The main contributions of our work are
the discovery, analysis and validation of systematic diffusion as the missing
concept which is instrumental in understanding and resolving these
contradictory findings. This systematic diffusion essentially curtails the
benefits of distilling from an LS-trained teacher, thereby rendering KD at
increased temperatures ineffective. Our discovery is comprehensively supported
by large-scale experiments, analyses and case studies including image
classification, neural machine translation and compact student distillation
tasks spanning across multiple datasets and teacher-student architectures.
Based on our analysis, we suggest practitioners to use an LS-trained teacher
with a low-temperature transfer to achieve high performance students. Code and
models are available at
https://keshik6.github.io/revisiting-ls-kd-compatibility/
