  Supervised deep learning methods require a large repository of annotated
data; hence, label noise is inevitable. Training with such noisy data
negatively impacts the generalization performance of deep neural networks. To
combat label noise, recent state-of-the-art methods employ some sort of sample
selection mechanism to select a possibly clean subset of data. Next, an
off-the-shelf semi-supervised learning method is used for training where
rejected samples are treated as unlabeled data. Our comprehensive analysis
shows that current selection methods disproportionately select samples from
easy (fast learnable) classes while rejecting those from relatively harder
ones. This creates class imbalance in the selected clean set and in turn,
deteriorates performance under high label noise. In this work, we propose
UNICON, a simple yet effective sample selection method which is robust to high
label noise. To address the disproportionate selection of easy and hard
samples, we introduce a Jensen-Shannon divergence based uniform selection
mechanism which does not require any probabilistic modeling and hyperparameter
tuning. We complement our selection method with contrastive learning to further
combat the memorization of noisy labels. Extensive experimentation on multiple
benchmark datasets demonstrates the effectiveness of UNICON; we obtain an 11.4%
improvement over the current state-of-the-art on CIFAR100 dataset with a 90%
noise rate. Our code is publicly available
