Improving fishing ground estimation with weak supervision and meta-learning

TL;DR

This paper improves fishing ground estimation by combining weak supervision and meta-learning to overcome limited catch data.

Contribution

A novel training strategy using weak supervision and meta-learning for fishing ground estimation with limited labeled data.

Findings

Using trajectory data with weak supervision improved model performance significantly.

Meta-learning helped reduce label noise during pre-training, enhancing overall accuracy.

The proposed method achieved a 64% improvement in F1-score over the baseline.

Abstract

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.

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