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对于内在可解释的CNN和视觉转换器的B-Cos对齐
IEEE transactions on pattern analysis and machine intelligence
|January 17, 2024
概括
我们引入了一个新的B-cos转换,通过对准权重与输入来提高深度神经网络 (DNN) 的解释性. 这种方法增强了特征突出显示和模型理解,而不会牺牲准确性.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 深度神经网络 (DNN) 通常缺乏可解释性.
- 了解DNN中的决策过程对于信任和调试至关重要.
研究的目的:
- 开发一种新的方法来提高深度神经网络 (DNN) 的解释性.
- 在DNN培训期间促进权重输入对齐.
主要方法:
- 提出了一个新的B-cos转换来取代DNN中的标准线性转换.
- 证明了一系列B-cos转换简化为单个可解释的线性转换.
- 集成B-cos转换到各种最先进的计算机视觉架构 (ResNets,DenseNets,ConvNeXt,视觉转换器) 中.
主要成果:
- B-cos转换促进了重量输入对齐,导致高度可解释的诱导线性转换.
- 这些转变有效地突出了与任务相关的特征.
- 将其集成到现有模型中,在ImageNet.Net上保持了高精度.
- 生成的解释显示出高视觉质量和强大的定量解释性指标.
结论:
- B-cos转换为提高DNN解释性提供了一个有希望的方向.
- 它与当前架构兼容,并增强功能相关性可视化.
- 这种方法提供了可解释但准确的深度学习模型.
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