  Learning robust feature representation from large-scale noisy faces stands
out as one of the key challenges in high-performance face recognition. Recent
attempts have been made to cope with this challenge by alleviating the
intra-class conflict and inter-class conflict. However, the unconstrained noise
type in each conflict still makes it difficult for these algorithms to perform
well. To better understand this, we reformulate the noise type of each class in
a more fine-grained manner as N-identities|K^C-clusters. Different types of
noisy faces can be generated by adjusting the values of \nkc. Based on this
unified formulation, we found that the main barrier behind the noise-robust
representation learning is the flexibility of the algorithm under different N,
K, and C. For this potential problem, we propose a new method, named Evolving
Sub-centers Learning~(ESL), to find optimal hyperplanes to accurately describe
the latent space of massive noisy faces. More specifically, we initialize M
sub-centers for each class and ESL encourages it to be automatically aligned to
N-identities|K^C-clusters faces via producing, merging, and dropping
operations. Images belonging to the same identity in noisy faces can
effectively converge to the same sub-center and samples with different
identities will be pushed away. We inspect its effectiveness with an elaborate
ablation study on the synthetic noisy dataset with different N, K, and C.
Without any bells and whistles, ESL can achieve significant performance gains
over state-of-the-art methods on large-scale noisy faces
