Deep convolutional neural networks have achieved remarkable success in face
recognition (FR), partly due to the abundant data availability. However, the
current training benchmarks exhibit an imbalanced quality distribution; most
images are of high quality. This poses issues for generalization on hard
samples since they are underrepresented during training. In this work, we
employ the multi-model boosting technique to deal with this issue. Inspired by
the well-known AdaBoost, we propose a sample-level weighting approach to
incorporate the importance of different samples into the FR loss. Individual
models of the proposed framework are experts at distinct levels of sample
hardness. Therefore, the combination of models leads to a robust feature
extractor without losing the discriminability on the easy samples. Also, for
incorporating the sample hardness into the training criterion, we analytically
show the effect of sample mining on the important aspects of current angular
margin loss functions, i.e., margin and scale. The proposed method shows
superior performance in comparison with the state-of-the-art algorithms in
extensive experiments on the CFP-FP, LFW, CPLFW, CALFW, AgeDB, TinyFace, IJB-B,
and IJB-C evaluation datasets.

利用多模型增强技术和样本级加权方法提出了一种强大的特征提取器，通过解决当前面部识别训练基准中高质量图片过多导致对困难样本泛化能力差的问题，以及将样本困难度纳入训练准则，取得了比其他算法更优的表现。

深層增強多模態集成人臉識別及樣本級權重

Deep Boosting Multi-Modal Ensemble Face Recognition with Sample-Level  Weighting

Multi-task learning (MTL) can improve the generalization performance of
neural networks by sharing representations with related tasks. Nonetheless, MTL
can also degrade performance through harmful interference between tasks. Recent
work has pursued task-specific loss weighting as a solution for this
interference. However, existing algorithms treat tasks as atomic, lacking the
ability to explicitly separate harmful and helpful signals beyond the task
level. To this end, we propose SLGrad, a sample-level weighting algorithm for
multi-task learning with auxiliary tasks. Through sample-specific task weights,
SLGrad reshapes the task distributions during training to eliminate harmful
auxiliary signals and augment useful task signals. Substantial generalization
performance gains are observed on (semi-) synthetic datasets and common
supervised multi-task problems.

提出了一种名为 SLGrad 的样本级别加权算法，通过样本特定的任务权重，改变训练过程中的任务分布，以消除有害的辅助信号并增强有用的任务信号，从而提高神经网络的泛化性能。