  We propose a framework using contrastive learning as a pre-training task to
perform image classification in the presence of noisy labels. Recent strategies
such as pseudo-labeling, sample selection with Gaussian Mixture models,
weighted supervised contrastive learning have been combined into a fine-tuning
phase following the pre-training. This paper provides an extensive empirical
study showing that a preliminary contrastive learning step brings a significant
gain in performance when using different loss functions: non-robust, robust,
and early-learning regularized. Our experiments performed on standard
benchmarks and real-world datasets demonstrate that: i) the contrastive
pre-training increases the robustness of any loss function to noisy labels and
ii) the additional fine-tuning phase can further improve accuracy but at the
cost of additional complexity.
