Generative adversarial network (GAN)-based vocoders have been intensively
studied because they can synthesize high-fidelity audio waveforms faster than
real-time. However, it has been reported that most GANs fail to obtain the
optimal projection for discriminating between real and fake data in the feature
space. In the literature, it has been demonstrated that slicing adversarial
network (SAN), an improved GAN training framework that can find the optimal
projection, is effective in the image generation task. In this paper, we
investigate the effectiveness of SAN in the vocoding task. For this purpose, we
propose a scheme to modify least-squares GAN, which most GAN-based vocoders
adopt, so that their loss functions satisfy the requirements of SAN. Through
our experiments, we demonstrate that SAN can improve the performance of
GAN-based vocoders, including BigVGAN, with small modifications. Our code is
available at this https URL

研究一种修改最小二乘 GAN 的方案以满足切片对抗网络（SAN）要求，通过实验验证切片对抗网络对基于 GAN 的合成器，包括 BigVGAN 的性能改进。

BigVSAN：利用切片对抗网络增强基于 GAN 的神经声码器

BigVSAN: Enhancing GAN-based Neural Vocoders with Slicing Adversarial  Network

Black-box neural network models are widely used in industry and science, yet
are hard to understand and interpret. Recently, the attention mechanism was
introduced, offering insights into the inner workings of neural language
models. This paper explores the use of attention-based neural networks
mechanism for estimating feature importance, as means for explaining the models
learned from propositional (tabular) data. Feature importance estimates,
assessed by the proposed Self-Attention Network (SAN) architecture, are
compared with the established ReliefF, Mutual Information and Random
Forest-based estimates, which are widely used in practice for model
interpretation. For the first time we conduct scale-free comparisons of feature
importance estimates across algorithms on ten real and synthetic data sets to
study the similarities and differences of the resulting feature importance
estimates, showing that SANs identify similar high-ranked features as the other
methods. We demonstrate that SANs identify feature interactions which in some
cases yield better predictive performance than the baselines, suggesting that
attention extends beyond interactions of just a few key features and detects
larger feature subsets relevant for the considered learning task.

本文旨在探索利用基于注意力机制的神经网络对特征进行重要性排序以用于模型解释的方法，通过对十个数据集进行规模无关的特征重要性估计算法比较的研究，展示了利用自注意力网络（SAN）对特征进行排序与其他方法在高级别特征识别上的相似性，以及在某些情况下，SAN 对特征交互性的识别比现有基线方法更具优势。

自注意力网络的特征重要性估计

Feature Importance Estimation with Self-Attention Networks

Most of the recent successful methods in accurate object detection build on
the convolutional neural networks (CNN). However, due to the lack of scale
normalization in CNN-based detection methods, the activated channels in the
feature space can be completely different according to a scale and this
difference makes it hard for the classifier to learn samples. We propose a
Scale Aware Network (SAN) that maps the convolutional features from the
different scales onto a scale-invariant subspace to make CNN-based detection
methods more robust to the scale variation, and also construct a unique
learning method which considers purely the relationship between channels
without the spatial information for the efficient learning of SAN. To show the
validity of our method, we visualize how convolutional features change
according to the scale through a channel activation matrix and experimentally
show that SAN reduces the feature differences in the scale space. We evaluate
our method on VOC PASCAL and MS COCO dataset. We demonstrate SAN by conducting
several experiments on structures and parameters. The proposed SAN can be
generally applied to many CNN-based detection methods to enhance the detection
accuracy with a slight increase in the computing time.

提出了一种尺度感知网络 (SAN)，将卷积特征从不同的尺度映射到尺度不变的子空间，使基于 CNN 检测方法更具鲁棒性。该方法有效地减少了尺度空间内的特征差异，可以应用于许多基于 CNN 的检测方法中，提高检测精度并略微增加计算时间。