通过反类和一次冷的交叉损失诱导神经崩
概括
我们介绍了One-Cold CE (OCCE) 损失,这是一种通过使用非目标类信息来改进分类的新方法. 这种方法提高了模型的概括性和在各种任务中的性能.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 标准软max交叉 (CE) 损失忽略了非目标类之间的关系,使优化信息未被利用.
- 这种限制阻碍了模型性能,因为它无法有效地利用补充类数据.
研究的目的:
- 提出一种新的损失函数,即一次冷CE (OCCE) 损失,以解决标准CE损失的限制.
- 构建互补类的激活,以改善特征表示.
- 在各种机器学习任务中增强模型概括和性能.
主要方法:
- 定义了每个目标类的"反类",包括所有非目标实例,包括补充类和分布外样本.
- 对每个反类实施了统一的单一冷编码分发目标.
- 在优化过程中鼓励模型在所有非目标类中均分配激活.
主要成果:
- 在特征空间中推广了类的对称几何结构.
- 在训练期间增加神经崩 (NC) 的程度.
- 解决了神经网络中的独立性缺陷问题,从而改善了概括性.
结论:
- 拟议的OCCE损失始终提高了分类,开放式识别和分布外检测任务的性能.
- OCCE损失有效地利用来自互补类的信息,从而产生更强大,更可通用的模型.
- 这种新的方法为监督分类和相关任务提供了相对于标准CE损失的显著改进.
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