Nowadays, most classification networks use one-hot encoding to represent categorical data because of its simplicity. However, one-hot encoding may affect the generalization ability as it neglects inter-class correlations. We observe that, even when a neural network trained with one-hot labels produces incorrect predictions, it still pays attention to the target image region and reveals which classes confuse the network. Inspired by this observation, we propose a confusion-focusing mechanism to address the class-confusion issue. Our confusion-focusing mechanism is implemented by a two-branch network architecture. Its baseline branch generates confusing classes, and its FocusNet branch, whose architecture is flexible, discriminates correct labels from these confusing classes. We also introduce a novel focus-picking loss function to improve classification accuracy by encouraging FocusNet to focus on the most confusing classes. The experimental results validate that our FocusNet is effective for image classification on common datasets, and that our focus-picking loss function can also benefit the current neural networks in improving their classification accuracy.
翻译:目前,大多数分类网络都使用一热编码来代表绝对数据,因为其简单性。然而,一热编码可能会影响一般化能力,因为它忽视了各等级之间的关联性。我们观察到,即使受过单热标签训练的神经网络产生不正确的预测,它仍然关注目标图像区域,并揭示哪些类别混淆网络。根据这项观察,我们建议了一个混乱焦点机制来解决分类融合问题。我们的混淆焦点机制由一个两权网络结构实施。它的基线分支产生混淆的类别,而它的FocusNet分支(其结构是灵活的)则对这些令人困惑的类别进行区分。我们还引入了一个新的重点选择损失功能,通过鼓励焦点网络关注最混乱的类别来提高分类的准确性。实验结果证实我们的焦点网络对于普通数据集的图像分类有效,而且我们的焦点选择损失功能也有利于当前的神经网络提高其分类准确性。