  Label noise significantly degrades the generalization ability of deep models
in applications. Effective strategies and approaches, \textit{e.g.}
re-weighting, or loss correction, are designed to alleviate the negative impact
of label noise when training a neural network. Those existing works usually
rely on the pre-specified architecture and manually tuning the additional
hyper-parameters. In this paper, we propose warped probabilistic inference
(WarPI) to achieve adaptively rectifying the training procedure for the
classification network within the meta-learning scenario. In contrast to the
deterministic models, WarPI is formulated as a hierarchical probabilistic model
by learning an amortization meta-network, which can resolve sample ambiguity
and be therefore more robust to serious label noise. Unlike the existing
approximated weighting function of directly generating weight values from
losses, our meta-network is learned to estimate a rectifying vector from the
input of the logits and labels, which has the capability of leveraging
sufficient information lying in them. This provides an effective way to rectify
the learning procedure for the classification network, demonstrating a
significant improvement of the generalization ability. Besides, modeling the
rectifying vector as a latent variable and learning the meta-network can be
seamlessly integrated into the SGD optimization of the classification network.
We evaluate WarPI on four benchmarks of robust learning with noisy labels and
achieve the new state-of-the-art under variant noise types. Extensive study and
analysis also demonstrate the effectiveness of our model.
