  Learning with noisy labels aims to ensure model generalization given a
label-corrupted training set. The sample selection strategy achieves promising
performance by selecting a label-reliable subset for model training. In this
paper, we empirically reveal that existing sample selection methods suffer from
both data and training bias that are represented as imbalanced selected sets
and accumulation errors in practice, respectively. However, only the training
bias was handled in previous studies. To address this limitation, we propose a
noIse-Tolerant Expert Model (ITEM) for debiased learning in sample selection.
Specifically, to mitigate the training bias, we design a robust network
architecture that integrates with multiple experts. Compared with the
prevailing double-branch network, our network exhibits better performance of
selection and prediction by ensembling these experts while training with fewer
parameters. Meanwhile, to mitigate the data bias, we propose a mixed sampling
strategy based on two weight-based data samplers. By training on the mixture of
two class-discriminative mini-batches, the model mitigates the effect of the
imbalanced training set while avoiding sparse representations that are easily
caused by sampling strategies. Extensive experiments and analyses demonstrate
the effectiveness of ITEM. Our code is available at this url
\href{https://github.com/1998v7/ITEM}{ITEM}.
