  To discover intrinsic inter-class transition probabilities underlying data,
learning with noise transition has become an important approach for robust deep
learning on corrupted labels. Prior methods attempt to achieve such transition
knowledge by pre-assuming strongly confident anchor points with 1-probability
belonging to a specific class, generally infeasible in practice, or directly
jointly estimating the transition matrix and learning the classifier from the
noisy samples, always leading to inaccurate estimation misguided by wrong
annotation information especially in large noise cases. To alleviate these
issues, this study proposes a new meta-transition-learning strategy for the
task. Specifically, through the sound guidance of a small set of meta data with
clean labels, the noise transition matrix and the classifier parameters can be
mutually ameliorated to avoid being trapped by noisy training samples, and
without need of any anchor point assumptions. Besides, we prove our method is
with statistical consistency guarantee on correctly estimating the desired
transition matrix. Extensive synthetic and real experiments validate that our
method can more accurately extract the transition matrix, naturally following
its more robust performance than prior arts. Its essential relationship with
label distribution learning is also discussed, which explains its fine
performance even under no-noise scenarios.
