Auditory Attention Detection (AAD) aims to detect target speaker from brain
signals in a multi-speaker environment. Although EEG-based AAD methods have
shown promising results in recent years, current approaches primarily rely on
traditional convolutional neural network designed for processing Euclidean data
like images. This makes it challenging to handle EEG signals, which possess
non-Euclidean characteristics. In order to address this problem, this paper
proposes a dynamical graph self-distillation (DGSD) approach for AAD, which
does not require speech stimuli as input. Specifically, to effectively
represent the non-Euclidean properties of EEG signals, dynamical graph
convolutional networks are applied to represent the graph structure of EEG
signals, which can also extract crucial features related to auditory spatial
attention in EEG signals. In addition, to further improve AAD detection
performance, self-distillation, consisting of feature distillation and
hierarchical distillation strategies at each layer, is integrated. These
strategies leverage features and classification results from the deepest
network layers to guide the learning of shallow layers. Our experiments are
conducted on two publicly available datasets, KUL and DTU. Under a 1-second
time window, we achieve results of 90.0\% and 79.6\% accuracy on KUL and DTU,
respectively. We compare our DGSD method with competitive baselines, and the
experimental results indicate that the detection performance of our proposed
DGSD method is not only superior to the best reproducible baseline but also
significantly reduces the number of trainable parameters by approximately 100
times.

本文提出了一种动态图自蒸馏的听觉注意力检测方法，该方法通过应用动态图卷积神经网络处理非欧几里德特性的脑电图信号，提取与听觉空间注意力相关的关键特征，并利用特征和分类结果指导浅层网络的学习，实验结果表明该方法优于基线方法并且参数数量减少 100 倍。

DGSD: 动态图自蒸馏用于基于 EEG 的听觉空间注意力检测

DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial  Attention Detection

Rain streaks bring serious blurring and visual quality degradation, which
often vary in size, direction and density. Current CNN-based methods achieve
encouraging performance, while are limited to depict rain characteristics and
recover image details in the poor visibility environment. To address these
issues, we present a Multi-scale Hourglass Hierarchical Fusion Network
(MH2F-Net) in end-to-end manner, to exactly captures rain streak features with
multi-scale extraction, hierarchical distillation and information aggregation.
For better extracting the features, a novel Multi-scale Hourglass Extraction
Block (MHEB) is proposed to get local and global features across different
scales through down- and up-sample process. Besides, a Hierarchical Attentive
Distillation Block (HADB) then employs the dual attention feature responses to
adaptively recalibrate the hierarchical features and eliminate the redundant
ones. Further, we introduce a Residual Projected Feature Fusion (RPFF) strategy
to progressively discriminate feature learning and aggregate different features
instead of directly concatenating or adding. Extensive experiments on both
synthetic and real rainy datasets demonstrate the effectiveness of the designed
MH2F-Net by comparing with recent state-of-the-art deraining algorithms. Our
source code will be available on the GitHub:
this https URL

提出了一种多尺度 Hourglass 分层融合网络 (MH2F-Net)，通过多尺度提取、分层蒸馏和信息聚合来准确捕捉雨滴特征，采用残差投影特征融合策略逐步区分特征学习和聚合不同特征，与最新的去雨算法相比在合成和真实的数据集上展示了其有效性。