  Deep Neural Networks (DNNs) have been shown to be susceptible to memorization
or overfitting in the presence of noisily-labelled data. For the problem of
robust learning under such noisy data, several algorithms have been proposed. A
prominent class of algorithms rely on sample selection strategies wherein,
essentially, a fraction of samples with loss values below a certain threshold
are selected for training. These algorithms are sensitive to such thresholds,
and it is difficult to fix or learn these thresholds. Often, these algorithms
also require information such as label noise rates which are typically
unavailable in practice. In this paper, we propose an adaptive sample selection
strategy that relies only on batch statistics of a given mini-batch to provide
robustness against label noise. The algorithm does not have any additional
hyperparameters for sample selection, does not need any information on noise
rates and does not need access to separate data with clean labels. We
empirically demonstrate the effectiveness of our algorithm on benchmark
datasets.
