Abstract
Benefit from large-scale training datasets, deep Convolutional Neural
Networks(CNNs) have achieved impressive results in face recognition(FR).
However, tremendous scale of datasets inevitably lead to noisy data, which
obviously reduce the performance of the trained CNN models. Kicking out wrong
labels from large-scale FR datasets is still very expensive, although some
cleaning approaches are proposed. According to the analysis of the whole
process of training CNN models supervised by angular margin based loss(AM-Loss)
functions, we find that the $þeta$ distribution of training samples
implicitly reflects their probability of being clean. Thus, we propose a novel
training paradigm that employs the idea of weighting samples based on the above
probability. Without any prior knowledge of noise, we can train high
performance CNN models with large-scale FR datasets. Experiments demonstrate
the effectiveness of our training paradigm. The codes are available at
https://github.com/huangyangyu/NoiseFace.
Users
Please
log in to take part in the discussion (add own reviews or comments).