Facial expression recognition (FER) models are typically trained on datasets
with a fixed number of seven basic classes. However, recent research works
point out that there are far more expressions than the basic ones. Thus, when
these models are deployed in the real world, they may encounter unknown
classes, such as compound expressions that cannot be classified into existing
basic classes. To address this issue, we propose the open-set FER task for the
first time. Though there are many existing open-set recognition methods, we
argue that they do not work well for open-set FER because FER data are all
human faces with very small inter-class distances, which makes the open-set
samples very similar to close-set samples. In this paper, we are the first to
transform the disadvantage of small inter-class distance into an advantage by
proposing a new way for open-set FER. Specifically, we find that small
inter-class distance allows for sparsely distributed pseudo labels of open-set
samples, which can be viewed as symmetric noisy labels. Based on this novel
observation, we convert the open-set FER to a noisy label detection problem. We
further propose a novel method that incorporates attention map consistency and
cycle training to detect the open-set samples. Extensive experiments on various
FER datasets demonstrate that our method clearly outperforms state-of-the-art
open-set recognition methods by large margins. Code is available at
this https URL

我们提出了一种新的面部表情识别模型，将小类间距的劣势转化为优势，通过注意力图的一致性和循环训练来检测开放集样本，实验证明我们的方法在各种面部表情识别数据集上明显优于现有的开放集识别方法。

开放集合人脸表情识别

Open-Set Facial Expression Recognition

The success of deep neural networks requires both high annotation quality and
massive data. However, the size and the quality of a dataset are usually a
trade-off in practice, as data collection and cleaning are expensive and
time-consuming. Therefore, automatic noisy label detection (NLD) techniques are
critical to real-world applications, especially those using crowdsourcing
datasets. As this is an under-explored topic in automatic speaker verification
(ASV), we present a simple but effective solution to the task. First, we
compare the effectiveness of various commonly used metric learning loss
functions under different noise settings. Then, we propose two ranking-based
NLD methods, inter-class inconsistency and intra-class inconsistency ranking.
They leverage the inconsistent nature of noisy labels and show high detection
precision even under a high level of noise. Our solution gives rise to both
efficient and effective cleaning of large-scale speaker recognition datasets.

本文探讨了深度神经网络在标注质量和数据量方面的要求，提出了两种基于排名的噪声标签检测方法，并比较了不同噪声情况下常用度量学习损失函数的有效性。该方法能够有效地清理大规模发言者识别数据集。

说话人识别中的噪声标签检测

Noisy Label Detection for Speaker Recognition

Modern deep neural networks (DNNs) become frail when the datasets contain
noisy (incorrect) class labels. Robust techniques in the presence of noisy
labels can be categorized into two folds: developing noise-robust functions or
using noise-cleansing methods by detecting the noisy data. Recently,
noise-cleansing methods have been considered as the most competitive
noisy-label learning algorithms. Despite their success, their noisy label
detectors are often based on heuristics more than a theory, requiring a robust
classifier to predict the noisy data with loss values. In this paper, we
propose a novel detector for filtering label noise. Unlike most existing
methods, we focus on each data's latent representation dynamics and measure the
alignment between the latent distribution and each representation using the
eigendecomposition of the data gram matrix. Our framework, coined as filtering
noisy instances via their eigenvectors (FINE), provides a robust detector with
derivative-free simple methods having theoretical guarantees. Under our
framework, we propose three applications of the FINE: sample-selection
approach, semi-supervised learning approach, and collaboration with
noise-robust loss functions. Experimental results show that the proposed
methods consistently outperform corresponding baselines for all three
applications on various benchmark datasets.

提出了一种基于数据奇异值分解的噪声标签检测方法，即使用噪声数据与潜在表征之间的对齐度测量来过滤噪声实例，应用于半监督学习、噪声鲁棒性损失函数等方面，并在基准数据集上验证了其较基准算法更为优越的性能。

使用有噪标签的 FINE 样本学习

FINE Samples for Learning with Noisy Labels

Deep neural networks (DNNs) are powerful tools in computer vision tasks.
However, in many realistic scenarios label noise is prevalent in the training
images, and overfitting to these noisy labels can significantly harm the
generalization performance of DNNs. We propose a novel technique to identify
data with noisy labels based on the different distributions of the predictive
uncertainties from a DNN over the clean and noisy data. Additionally, the
behavior of the uncertainty over the course of training helps to identify the
network weights which best can be used to relabel the noisy labels. Data with
noisy labels can therefore be cleaned in an iterative process. Our proposed
method can be easily implemented, and shows promising performance on the task
of noisy label detection on CIFAR-10 and CIFAR-100.

本文提出了一种基于深度神经网络的预测不确定性来识别训练数据中存在的噪声标签并通过迭代程序实现清洗的方法，该方法在 CIFAR-10 和 CIFAR-100 上有良好的性能表现。