CrossSiam: k-Fold Cross Representation Learning
CrossSiam: k-Fold Cross Representation Learning
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DOI：
10.5220/0010972500003116
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Kaiyu Suzuki;Y. Kambayashi;Tomofumi Matsuzawa
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文献类型：
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作者：
Kaiyu Suzuki;Y. Kambayashi;Tomofumi Matsuzawa
无人机等多智能体最重要的任务之一是根据机载摄像头捕获的图像自动做出决策。这些代理必须高度准确和可靠。为此，我们将k-fold交叉验证应用于使用深度学习对图像进行分类的任务，这是一种比较和评估模型的方法;这种技术易于理解和实现，并且可以产生较低的偏差估计。然而，k折交叉验证减少了每个神经网络的数据量，从而降低了准确性。为了解决这个问题，我们提出了CrossSiam。CrossSiam是一种表示学习方法，用于训练特征编码器以模仿每个神经网络的验证数据的嵌入空间。我们表明，该方法具有更高的分类精度比ParaSiam（基线）。这种方法在需要可靠性的领域非常重要，例如灾难情况下的自动驾驶汽车和无人机。
: One of the most important tasks for multi-agents such as drones is to automatically make decisions based on images captured by on-board cameras. These agents must be highly accurate and reliable. For this purpose, we applied k-fold cross validation to the task of classifying images using deep learning, which is a method that compares and evaluates models appropriately model of a given problem; this technique is easy to understand and easy to implement, and it produces results in lower bias estimates. However, k-fold cross validation reduces the amount of data per neural network, which reduces the accuracy. In order to address this problem, we propose CrossSiam. CrossSiam is a one of the representation learning methods to train feature encoders to mimic the embedding space of the validation data of each neural network. We show that the proposed method has a higher classification accuracy than the ParaSiam (baseline). This approach can be very important in the field where reliability is required, such as automated vehicles and drones in disaster situations.