Recognition in low quality face datasets is challenging because facial
attributes are obscured and degraded. Advances in margin-based loss functions
have resulted in enhanced discriminability of faces in the embedding space.
Further, previous studies have studied the effect of adaptive losses to assign
more importance to misclassified (hard) examples. In this work, we introduce
another aspect of adaptiveness in the loss function, namely the image quality.
We argue that the strategy to emphasize misclassified samples should be
adjusted according to their image quality. Specifically, the relative
importance of easy or hard samples should be based on the sample's image
quality. We propose a new loss function that emphasizes samples of different
difficulties based on their image quality. Our method achieves this in the form
of an adaptive margin function by approximating the image quality with feature
norms. Extensive experiments show that our method, AdaFace, improves the face
recognition performance over the state-of-the-art (SoTA) on four datasets
(IJB-B, IJB-C, IJB-S and TinyFace). Code and models are released in
this https URL

本研究提出了一种新的损失函数 AdaFace，它可以基于图像质量加重不同难度的样本，通过用特征范数来近似图像质量，从而提高人脸识别性能。

AdaFace：用于人脸识别的质量自适应边距

AdaFace: Quality Adaptive Margin for Face Recognition

Pseudo-labels are confident predictions made on unlabeled target data by a
classifier trained on labeled source data. They are widely used for adapting a
model to unlabeled data, e.g., in a semi-supervised learning setting.
Our key insight is that pseudo-labels are naturally imbalanced due to
intrinsic data similarity, even when a model is trained on balanced source data
and evaluated on balanced target data. If we address this previously unknown
imbalanced classification problem arising from pseudo-labels instead of
ground-truth training labels, we could remove model biases towards false
majorities created by pseudo-labels.
We propose a novel and effective debiased learning method with pseudo-labels,
based on counterfactual reasoning and adaptive margins: The former removes the
classifier response bias, whereas the latter adjusts the margin of each class
according to the imbalance of pseudo-labels. Validated by extensive
experimentation, our simple debiased learning delivers significant accuracy
gains over the state-of-the-art on ImageNet-1K: 26% for semi-supervised
learning with 0.2% annotations and 9% for zero-shot learning. Our code is
available at: this https URL.

本文提出一种基于对抗性反事实推理和自适应边界的偏差学习方法，旨在解决伪标签自然不平衡的分类问题。通过对大量实验数据的验证，实现了对 ImageNet-1K 进行半监督和零样本学习的显着精度提升。