  Deep neural networks trained with standard cross-entropy loss memorize noisy
labels, which degrades their performance. Most research to mitigate this
memorization proposes new robust classification loss functions. Conversely, we
propose a Multi-Objective Interpolation Training (MOIT) approach that jointly
exploits contrastive learning and classification to mutually help each other
and boost performance against label noise. We show that standard supervised
contrastive learning degrades in the presence of label noise and propose an
interpolation training strategy to mitigate this behavior. We further propose a
novel label noise detection method that exploits the robust feature
representations learned via contrastive learning to estimate per-sample
soft-labels whose disagreements with the original labels accurately identify
noisy samples. This detection allows treating noisy samples as unlabeled and
training a classifier in a semi-supervised manner to prevent noise memorization
and improve representation learning. We further propose MOIT+, a refinement of
MOIT by fine-tuning on detected clean samples. Hyperparameter and ablation
studies verify the key components of our method. Experiments on synthetic and
real-world noise benchmarks demonstrate that MOIT/MOIT+ achieves
state-of-the-art results. Code is available at https://git.io/JI40X.
