  Deep neural networks have shown great success in representation learning.
However, when learning with noisy labels (LNL), they can easily overfit and
fail to generalize to new data. This paper introduces a simple and effective
method, named Learning to Bootstrap (L2B), which enables models to bootstrap
themselves using their own predictions without being adversely affected by
erroneous pseudo-labels. It achieves this by dynamically adjusting the
importance weight between real observed and generated labels, as well as
between different samples through meta-learning. Unlike existing instance
reweighting methods, the key to our method lies in a new, versatile objective
that enables implicit relabeling concurrently, leading to significant
improvements without incurring additional costs.
  L2B offers several benefits over the baseline methods. It yields more robust
models that are less susceptible to the impact of noisy labels by guiding the
bootstrapping procedure more effectively. It better exploits the valuable
information contained in corrupted instances by adapting the weights of both
instances and labels. Furthermore, L2B is compatible with existing LNL methods
and delivers competitive results spanning natural and medical imaging tasks
including classification and segmentation under both synthetic and real-world
noise. Extensive experiments demonstrate that our method effectively mitigates
the challenges of noisy labels, often necessitating few to no validation
samples, and is well generalized to other tasks such as image segmentation.
This not only positions it as a robust complement to existing LNL techniques
but also underscores its practical applicability. The code and models are
available at https://github.com/yuyinzhou/l2b.
