  Supervised learning of deep neural networks heavily relies on large-scale
datasets annotated by high-quality labels. In contrast, mislabeled samples can
significantly degrade the generalization of models and result in memorizing
samples, further learning erroneous associations of data contents to incorrect
annotations. To this end, this paper proposes an efficient approach to tackle
noisy labels by learning robust feature representation based on unsupervised
augmentation restoration and cluster regularization. In addition, progressive
self-bootstrapping is introduced to minimize the negative impact of supervision
from noisy labels. Our proposed design is generic and flexible in applying to
existing classification architectures with minimal overheads. Experimental
results show that our proposed method can efficiently and effectively enhance
model robustness under severely noisy labels.
