  Robust loss minimization is an important strategy for handling robust
learning issue on noisy labels. Current approaches for designing robust losses
involve the introduction of noise-robust factors, i.e., hyperparameters, to
control the trade-off between noise robustness and learnability. However,
finding suitable hyperparameters for different datasets with noisy labels is a
challenging and time-consuming task. Moreover, existing robust loss methods
usually assume that all training samples share common hyperparameters, which
are independent of instances. This limits the ability of these methods to
distinguish the individual noise properties of different samples and overlooks
the varying contributions of diverse training samples in helping models
understand underlying patterns. To address above issues, we propose to assemble
robust loss with instance-dependent hyperparameters to improve their noise
tolerance with theoretical guarantee. To achieve setting such
instance-dependent hyperparameters for robust loss, we propose a meta-learning
method which is capable of adaptively learning a hyperparameter prediction
function, called Noise-Aware-Robust-Loss-Adjuster (NARL-Adjuster for brevity).
Through mutual amelioration between hyperparameter prediction function and
classifier parameters in our method, both of them can be simultaneously finely
ameliorated and coordinated to attain solutions with good generalization
capability. Four SOTA robust loss functions are attempted to be integrated with
our algorithm, and comprehensive experiments substantiate the general
availability and effectiveness of the proposed method in both its noise
tolerance and performance.
