通过量化原型向量实现稳定可解释的图像分类，无需微调主干网络。
ProtoQuant: Quantization of Prototypical Parts For General and Fine-Grained Image Classification
- 用离散码本约束原型，提升模型稳定性与可解释性。
- 在ImageNet和细粒度数据集上达到竞争性准确率。
- 适合需要可解释性且追求高效推理的应用场景。
原型部件模型提供了一种‘看起来像那样’的内在可解释范式，但通常在ImageNet规模的数据上泛化能力不足，且常需计算开销大的主干网络微调。此外，现有方法常出现‘原型漂移’问题，即学习到的原型在训练分布中缺乏实体依据，对微小扰动敏感。本文提出ProtoQuant，通过潜在空间中的向量量化实现原型稳定性和有据可依的可解释性。将原型限制在潜在空间中学习到的离散码本内，确保其忠实反映训练数据，无需更新主干网络。该设计使ProtoQuant能作为高效、可解释的分类头，扩展至大规模数据集。我们在ImageNet及多个细粒度基准（CUB-200、Cars-196）上评估了ProtoQuant，结果表明其在保持竞争力分类准确率的同时，实现了对ImageNet的泛化，并在可解释性指标上与其它原型部件方法相当。
原文摘要 · Abstract (English)
Prototypical parts-based models offer a "this looks like that" paradigm for intrinsic interpretability, yet they typically struggle with ImageNet-scale generalization and often require computationally expensive backbone finetuning. Furthermore, existing methods frequently suffer from "prototype drift," where learned prototypes lack tangible grounding in the training distribution and change their activation under small perturbations. We present ProtoQuant, a novel architecture that achieves prototype stability and grounded interpretability through latent vector quantization. By constraining prototypes to a discrete learned codebook within the latent space, we ensure they remain faithful representations of the training data without the need to update the backbone. This design allows ProtoQuant to function as an efficient, interpretable head that scales to large-scale datasets. We evaluate ProtoQuant on ImageNet and several fine-grained benchmarks (CUB-200, Cars-196). Our results demonstrate that ProtoQuant achieves competitive classification accuracy while generalizing to ImageNet and comparable interpretability metrics to other prototypical-parts-based methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。