论文标题
来自图像标记的卷积神经网络
Convolutional Neural Networks from Image Markers
论文作者
论文摘要
最近提出了一种从图像标记（FLIM）学习的功能学习的技术，以估算卷积过滤器，而没有反向传播，从用户在每个类别上绘制的中风（例如1-3），每类的笔触，并用于椰子树图像分类。本文扩展了完全连接的图层的FLIM，并在不同的图像分类问题上进行了证明。该工作评估了来自多个用户的标记选择以及添加完全连接层的影响。结果表明，基于FLIM的卷积神经网络可以胜过相同的架构，该体系结构通过反向传播从头开始训练。
A technique named Feature Learning from Image Markers (FLIM) was recently proposed to estimate convolutional filters, with no backpropagation, from strokes drawn by a user on very few images (e.g., 1-3) per class, and demonstrated for coconut-tree image classification. This paper extends FLIM for fully connected layers and demonstrates it on different image classification problems. The work evaluates marker selection from multiple users and the impact of adding a fully connected layer. The results show that FLIM-based convolutional neural networks can outperform the same architecture trained from scratch by backpropagation.
