This work focuses on plant leaf disease classification and explores three crucial aspects: adversarial training, model explainability, and model compression. The models' robustness against adversarial attacks is enhanced through adversarial training, ensuring accurate classification even in the presence of threats. Leveraging explainability techniques, we gain insights into the model's decision-making process, improving trust and transparency. Additionally, we explore model compression techniques to optimize computational efficiency while maintaining classification performance. Through our experiments, we determine that on a benchmark dataset, the robustness can be the price of the classification accuracy with performance reductions of 3%-20% for regular tests and gains of 50%-70% for adversarial attack tests. We also demonstrate that a student model can be 15-25 times more computationally efficient for a slight performance reduction, distilling the knowledge of more complex models.

翻译：本文聚焦于植物叶片病害分类，并探讨了三个关键方面：对抗训练、模型可解释性及模型压缩。通过对抗训练增强模型对对抗攻击的鲁棒性，确保即便在存在威胁时仍能实现准确分类。借助可解释性技术，我们深入理解模型的决策过程，提升了信任度与透明度。此外，我们研究了模型压缩技术，以优化计算效率的同时保持分类性能。通过实验发现，在基准数据集上，鲁棒性的提升可能以分类精度为代价：常规测试下性能下降3%-20%，而对抗攻击测试中性能提升50%-70%。我们还证明，学生模型可在性能轻微下降的情况下实现15-25倍的计算效率提升，从而蒸馏出更复杂模型的知识。