Annotating a qualitative large-scale facial expression dataset is extremely
difficult due to the uncertainties caused by ambiguous facial expressions,
low-quality facial images, and the subjectiveness of annotators. These
uncertainties lead to a key challenge of large-scale Facial Expression
Recognition (FER) in deep learning era. To address this problem, this paper
proposes a simple yet efficient Self-Cure Network (SCN) which suppresses the
uncertainties efficiently and prevents deep networks from over-fitting
uncertain facial images. Specifically, SCN suppresses the uncertainty from two
different aspects: 1) a self-attention mechanism over mini-batch to weight each
training sample with a ranking regularization, and 2) a careful relabeling
mechanism to modify the labels of these samples in the lowest-ranked group.
Experiments on synthetic FER datasets and our collected WebEmotion dataset
validate the effectiveness of our method. Results on public benchmarks
demonstrate that our SCN outperforms current state-of-the-art methods with
\textbf{88.14}\% on RAF-DB, \textbf{60.23}\% on AffectNet, and \textbf{89.35}\%
on FERPlus. The code will be available at
\href{https://github.com/kaiwang960112/Self-Cure-Network}{this https URL}.

该研究提出了一种基于 Self-Cure Network 的深度学习面部表情识别方法，通过权重排名和周到的重新标记机制抑制面部表情数据集中的不确定性，实现对不确定面部图像的过度拟合，并在公共基准测试中达到了优于当前最先进方法的分类精度。

大规模面部表情识别中的不确定性抑制

Suppressing Uncertainties for Large-Scale Facial Expression Recognition

In distributional reinforcement learning (RL), the estimated distribution of
value function models both the parametric and intrinsic uncertainties. We
propose a novel and efficient exploration method for deep RL that has two
components. The first is a decaying schedule to suppress the intrinsic
uncertainty. The second is an exploration bonus calculated from the upper
quantiles of the learned distribution. In Atari 2600 games, our method
outperforms QR-DQN in 12 out of 14 hard games (achieving 483 \% average gain
across 49 games in cumulative rewards over QR-DQN with a big win in Venture).
We also compared our algorithm with QR-DQN in a challenging 3D driving
simulator (CARLA). Results show that our algorithm achieves near-optimal safety
rewards twice faster than QRDQN.

本论文提出的一种深度深度强化学习探索方法具有两个组成部分：有一个递减的时间表来抑制内在不确定性，一个探索奖励是从所学分布的上分位数中计算出来的，实验结果表明，我们的算法在 14 个 Atari 2600 游戏中有 12 个游戏取得了比 QR-DQN 更好的成绩，在难度较大的 3D 驾驶模拟器 (CARLA) 中，我们的算法安全奖励达到了近乎最优，比 QRDQN 快两倍。