Effectiveness Of Multi-task Learning Research Articles

Experimental Results of Underwater Sound Speed Profile Inversion by Few-Shot Multi-Task Learning

Underwater Sound Speed Profile (SSP) distribution is crucial for the propagation mode of acoustic signals, so fast and accurate estimation of SSP is of great importance in building underwater observation systems. The state-of-the-art SSP inversion methods include frameworks of matched field processing (MFP), compressive sensing (CS), and feed-forward neural networks (FNNs), among which the FNN shows better real-time performance while maintaining the same level of accuracy. However, the training of FNN needs quite a lot historical SSP samples, which is difficult to satisfy in many ocean areas. This situation is called few-shot learning. To tackle this issue, we propose a multi-task learning (MTL) model with partial parameter sharing among different training tasks. By MTL, common features could be extracted, which accelerates the learning process on given tasks, and reduces the demand for reference samples, enhancing the generalization ability in few-shot learning. To verify the feasibility and effectiveness of MTL, a deep-ocean experiment was held in April 2023 in the South China Sea. Results show that MTL outperforms the other mainstream methods in terms of accuracy for SSP inversion, while inheriting the real-time advantage of FNN during the inversion stage.

A deep learning approach to automatic gingivitis screening based on classification and localization in RGB photos

Routine dental visit is the most common approach to detect the gingivitis. However, such diagnosis can sometimes be unavailable due to the limited medical resources in certain areas and costly for low-income populations. This study proposes to screen the existence of gingivitis and its irritants, i.e., dental calculus and soft deposits, from oral photos with a novel Multi-Task Learning convolutional neural network (CNN) model. The study can be meaningful for promoting the public dental health, since it sheds light on a cost-effective and ubiquitous solution for the early detection of dental issues. With 625 patients included in this study, the classification Area Under the Curve (AUC) for detecting gingivitis, dental calculus and soft deposits were 87.11%, 80.11%, and 78.57%, respectively; Meanwhile, according to our experiments, the model can also localize the three types of findings on oral photos with moderate accuracy, which enables the model to explain the screen results. By comparing to general-purpose CNNs, we showed our model significantly outperformed on both classification and localization tasks, which indicates the effectiveness of Multi-Task Learning on dental disease detection. In all, the study shows the potential of deep learning for enabling the screening of dental diseases among large populations.

Predominant Instrument Recognition Based on Deep Neural Network With Auxiliary Classification

Instrument recognition plays very important roles in music information retrieval, sound source separation and automatic music transcription. However, due to different playing styles and audio qualities, this task cannot be accomplished easily. Simultaneous existence of multiple instruments in polyphonic music increases the challenge to a greater extent. This article mainly focus on the identification of the predominant instruments in polyphonic music. We propose to construct a network with an auxiliary classification designed based on the onset groups and instrument families. The principal classification and the auxiliary classification enable the network to learn the instrument categories and groups jointly in a pattern of multitask learning. The IRMAS dataset is adopted in the experiment to extract the mel-spectrogram and six other types of features. The micro and macro average of precisions, recalls and F1 measures are used to evaluate the classification results. The effect of multitask learning, batch normalization and center loss in the predominant instrument recognition are demonstrated by various experiments. By selecting the loss ratios through a development set, the micro and macro F1 measures of our proposed network can reach 0.685 and 0.597, which are 10.7% and 16.4% higher than those obtained by the baseline, the ConvNet presented in [1] .