Deep neural networks enable real-time monitoring of in-vehicle driver,
facilitating the timely prediction of distractions, fatigue, and potential
hazards. This technology is now integral to intelligent transportation systems.
Recent research has exposed unreliable cross-dataset end-to-end driver behavior
recognition due to overfitting, often referred to as ``shortcut learning",
resulting from limited data samples. In this paper, we introduce the
Score-Softmax classifier, which addresses this issue by enhancing inter-class
independence and Intra-class uncertainty. Motivated by human rating patterns,
we designed a two-dimensional supervisory matrix based on marginal Gaussian
distributions to train the classifier. Gaussian distributions help amplify
intra-class uncertainty while ensuring the Score-Softmax classifier learns
accurate knowledge. Furthermore, leveraging the summation of independent
Gaussian distributed random variables, we introduced a multi-channel
information fusion method. This strategy effectively resolves the
multi-information fusion challenge for the Score-Softmax classifier.
Concurrently, we substantiate the necessity of transfer learning and
multi-dataset combination. We conducted cross-dataset experiments using the
SFD, AUCDD-V1, and 100-Driver datasets, demonstrating that Score-Softmax
improves cross-dataset performance without modifying the model architecture.
This provides a new approach for enhancing neural network generalization.
Additionally, our information fusion approach outperforms traditional methods.

基于 Score-Softmax 分类器的深度神经网络提高了驾驶员行为识别的泛化能力，并通过信息融合解决多信息融合挑战。

通过 Score-Softmax 分类器提升分心驾驶检测的跨数据集性能

Enhancing Cross-Dataset Performance of Distracted Driving Detection With  Score-Softmax Classifier

Current fake audio detection algorithms have achieved promising performances
on most datasets. However, their performance may be significantly degraded when
dealing with audio of a different dataset. The orthogonal weight modification
to overcome catastrophic forgetting does not consider the similarity of genuine
audio across different datasets. To overcome this limitation, we propose a
continual learning algorithm for fake audio detection to overcome catastrophic
forgetting, called Regularized Adaptive Weight Modification (RAWM). When
fine-tuning a detection network, our approach adaptively computes the direction
of weight modification according to the ratio of genuine utterances and fake
utterances. The adaptive modification direction ensures the network can
effectively detect fake audio on the new dataset while preserving its knowledge
of old model, thus mitigating catastrophic forgetting. In addition, genuine
audio collected from quite different acoustic conditions may skew their feature
distribution, so we introduce a regularization constraint to force the network
to remember the old distribution in this regard. Our method can easily be
generalized to related fields, like speech emotion recognition. We also
evaluate our approach across multiple datasets and obtain a significant
performance improvement on cross-dataset experiments.

我们提出了一种连续学习算法用于检测伪造音频以克服灾难性遗忘，并引入正则化约束来控制特征分布，通过自适应权重修改方向，网络可以在新数据集上有效检测伪造音频，同时保留对旧模型的知识，实现降低灾难性遗忘。我们在多个数据集上评估了我们的方法，并在跨数据集实验中取得了显著的性能改进。

克服虛假音頻檢測中的災難性遺忘

Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio  Detection

A continual learning solution is proposed to address the out-of-distribution
generalization problem for pedestrian detection. While recent pedestrian
detection models have achieved impressive performance on various datasets, they
remain sensitive to shifts in the distribution of the inference data. Our
method adopts and modifies Elastic Weight Consolidation to a backbone object
detection network, in order to penalize the changes in the model weights based
on their importance towards the initially learned task. We show that when
trained with one dataset and fine-tuned on another, our solution learns the new
distribution and maintains its performance on the previous one, avoiding
catastrophic forgetting. We use two popular datasets, CrowdHuman and
CityPersons for our cross-dataset experiments, and show considerable
improvements over standard fine-tuning, with a 9% and 18% miss rate percent
reduction improvement in the CrowdHuman and CityPersons datasets, respectively.

本论文提出了一种连续学习的解决方案，以解决行人检测的超出分布问题，并针对性能降低的问题制定了改进的 Elastic Weight Consolidation 方法。通过在一个数据集上进行训练和在另一个数据集上进行微调来达到学习新分布并在先前的数据集上保持性能的目的。