Scholarlink

<p>The agriculture and fisheries industry faces an increasing need to transition from labor-intensive, experience-based methods to a data-driven smart industry, enhancing productivity through automation and unmanned systems. This paper proposes a modified ConvNeXt2 CNN model that utilizes the triplet loss function, tailored for agricultural and marine products requiring quality standard adjustments. This approach addresses the limitations of traditional classification models that rely on softmax and cross-entropy by enabling the direct handling of feature vectors in the latent space, allowing quality criteria adjustments without retraining the model. The proposed method demonstrated superior classification performance on actual agricultural and marine product datasets and confirmed the feasibility of arranging individual items by quality grades, facilitating immediate quality standard adjustments in response to market conditions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Study on Adjusting Feature Vectors in Latent Space Using the Triplet Loss Function

  • 期刊论文

  • 发表日期：

    2025年5月14日

查看全文
  • Jungin Kim,
  • Chan-Young Yoon,
  • Kwang-Eun Ko,
  • Inhoon Jang

摘要

The agriculture and fisheries industry faces an increasing need to transition from labor-intensive, experience-based methods to a data-driven smart industry, enhancing productivity through automation and unmanned systems. This paper proposes a modified ConvNeXt2 CNN model that utilizes the triplet loss function, tailored for agricultural and marine products requiring quality standard adjustments. This approach addresses the limitations of traditional classification models that rely on softmax and cross-entropy by enabling the direct handling of feature vectors in the latent space, allowing quality criteria adjustments without retraining the model. The proposed method demonstrated superior classification performance on actual agricultural and marine product datasets and confirmed the feasibility of arranging individual items by quality grades, facilitating immediate quality standard adjustments in response to market conditions.