提出新型损失函数，让分布式网络只识别特定类别。
Distributed Convolutional Neural Networks for Object Recognition
- 将正样本映射到高维空间紧凑区域，负样本归零，实现特征解耦。
- 模型仅提取少数正类特征，结构轻量，测试泛化能力强。
- 适合复杂背景中定位特定目标，对未见类别也有效。
本文提出一种用于训练分布式卷积神经网络（DisCNN）的新损失函数，使其仅识别特定正类。通过将正样本映射至高维空间的紧凑集合，负样本映射至原点，DisCNN仅提取正类特征，实现正负类特征解耦。实验验证了该方法的有效性。模型因只需提取少量正类特征而具有轻量化架构，在测试数据上表现出色，对未见类别仍具有效性。此外，利用DisCNN可轻松实现嵌入于大而复杂背景中的正类目标检测。
原文摘要 · Abstract (English)
This paper proposes a novel loss function for training a distributed convolutional neural network (DisCNN) to recognize only a specific positive class. By mapping positive samples to a compact set in high-dimensional space and negative samples to Origin, the DisCNN extracts only the features of the positive class. An experiment is given to prove this. Thus, the features of the positive class are disentangled from those of the negative classes. The model has a lightweight architecture because only a few positive-class features need to be extracted. The model demonstrates excellent generalization on the test data and remains effective even for unseen classes. Finally, using DisCNN, object detection of positive samples embedded in a large and complex background is straightforward.
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