Takasan Kazuki, Iiyama Masaaki
Graduate School of Data Science, Shiga University, Hikone, Shiga, Japan.
PLoS One. 2025 Apr 11;20(4):e0321116. doi: 10.1371/journal.pone.0321116. eCollection 2025.
Estimating fishing grounds is an important task in the fishing industry. This study modeled the fisher's decision-making process based on sea surface temperature patterns as a pattern recognition task. We used a deep learning-based keypoint detector to estimate fishing ground locations from these patterns. However, training the model required catch data for annotation, the amount of which was limited. To address this, we proposed a training strategy that combines weak supervision and meta-learning to estimate fishing grounds. Weak supervision involves using partially annotated or noisy data, where the labels are incomplete or imprecise. In our case, catch data cover only a subset of fishing grounds, and trajectory data, which are readily available and larger in volume than catch data, provide imprecise representations of fishing grounds. Meta-learning helps the model adapt to the noise by refining its learning rate during training. Our approach involved pre-training with trajectory data and fine-tuning with catch data, with a meta-learner further mitigating label noise during pre-training. Experimental results showed that our method improved the F1-score by 64% compared to the baseline using only catch data, demonstrating the effectiveness of pre-training and meta-learning.
估算渔场是渔业中的一项重要任务。本研究将渔民的决策过程建模为基于海面温度模式的模式识别任务。我们使用基于深度学习的关键点检测器从这些模式中估算渔场位置。然而，训练模型需要用于标注的渔获数据，而其数量有限。为了解决这个问题，我们提出了一种结合弱监督和元学习来估算渔场的训练策略。弱监督涉及使用部分标注或有噪声的数据，其中标签是不完整或不准确的。在我们的案例中，渔获数据仅覆盖渔场的一个子集，而轨迹数据易于获取且数量比渔获数据大，它提供了渔场的不准确表示。元学习通过在训练期间调整学习率来帮助模型适应噪声。我们的方法包括使用轨迹数据进行预训练并使用渔获数据进行微调，同时一个元学习器在预训练期间进一步减轻标签噪声。实验结果表明，与仅使用渔获数据的基线相比，我们的方法将F1分数提高了64%，证明了预训练和元学习的有效性。
