论文标题
双流时空网络具有用于监测家用笼子中动物的功能共享
Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage
论文作者
论文摘要
本文提出了一种时空深度学习方法，用于家庭笼中的小鼠行为分类。我们使用一系列具有各种修改的双流式体系结构来提高性能，我们引入了一种新型的功能共享方法，该方法在整个网络中以常规的间隔共同处理流。为了研究这种方法的疗效，通过以与主要分类器相同的严格方式分离流和训练/测试来评估模型。使用基于Inception的网络和基于注意力的网络的合奏，使用单身小鼠的注释，公开可用的数据集，达到了86.47％的预测准确性，这两者都利用此功能共享。我们还通过消融研究证明，对于所有模型，特征共享体系结构始终比具有单独流的传统流相比，表现更好。在小鼠和人类的其他活动数据集上进一步评估了最佳性能模型。未来的工作将调查特征共享与行为分类的有效性，在无监督的异常检测域中。
This paper presents a spatiotemporal deep learning approach for mouse behavioural classification in the home-cage. Using a series of dual-stream architectures with assorted modifications to increase performance, we introduce a novel feature sharing approach that jointly processes the streams at regular intervals throughout the network. To investigate the efficacy of this approach, models were evaluated by dissociating the streams and training/testing in the same rigorous manner as the main classifiers. Using an annotated, publicly available dataset of a singly-housed mice, we achieve prediction accuracy of 86.47% using an ensemble of a Inception-based network and an attention-based network, both of which utilize this feature sharing. We also demonstrate through ablation studies that for all models, the feature-sharing architectures consistently perform better than conventional ones having separate streams. The best performing models were further evaluated on other activity datasets, both mouse and human. Future work will investigate the effectiveness of feature sharing to behavioural classification in the unsupervised anomaly detection domain.
