基于自适应原型视觉Transformer的可解释图像分类
Interpretable Image Classification with Adaptive Prototype-based Vision Transformers
AI总结：
ProtoViT结合视觉Transformer与可变形原型部分，实现可解释图像分类，性能优于现有原型方法。
摘要：
我们提出了ProtoViT，一种结合深度学习和基于案例推理的可解释图像分类方法。该方法通过将图像与一组学习到的原型进行比较来对图像进行分类，提供“这看起来像那”形式的解释。在我们的模型中，一个原型由多个部分（parts）组成，这些部分可以在不规则几何形状上变形，以在图像之间进行更好的比较。与依赖卷积神经网络（CNN）主干和空间刚性原型的现有模型不同，我们的模型将视觉Transformer（ViT）主干集成到基于原型的模型中，同时提供空间变形的原型，这些原型不仅适应对象的几何变化，而且通过自适应数量的原型部分提供连贯且清晰的原型特征表示。我们的实验表明，我们的模型通常能比现有的基于原型的模型获得更高的性能。我们的综合分析确保了原型的一致性和解释的忠实性。
英文摘要：
We present ProtoViT, a method for interpretable image classification combining deep learning and case-based reasoning. This method classifies an image by comparing it to a set of learned prototypes, providing explanations of the form ``this looks like that.'' In our model, a prototype consists of \textit{parts}, which can deform over irregular geometries to create a better comparison between images. Unlike existing models that rely on Convolutional Neural Network (CNN) backbones and spatially rigid prototypes, our model integrates Vision Transformer (ViT) backbones into prototype based models, while offering spatially deformed prototypes that not only accommodate geometric variations of objects but also provide coherent and clear prototypical feature representations with an adaptive number of prototypical parts. Our experiments show that our model can generally achieve higher performance than the existing prototype based models. Our comprehensive analyses ensure that the prototypes are consistent and the interpretations are faithful.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
