用判别分析提升图像分割边界清晰度，不增加推理开销。
Deep Image Segmentation via Discriminant Feature Learning
- 设计可微的判别特征损失，增强类间差异、缩小类内差异。
- 在DIS5K上多模型测试，边界更锐利，置信度更高。
- 适合追求高精度边界的分割任务，无需改架构。
准确的图像分割仍具挑战性，尤其在生成清晰、高置信度边界方面。尽管现代架构取得进展，但多数仍依赖交叉熵和Dice等标准损失函数，忽视了学习特征的判别结构，导致边界不准。本文提出深度判别分析（DDA），一种可微、与架构无关的损失函数，融合经典判别原理用于网络训练。DDA显式最大化类间方差，最小化类内方差，促进特征分布紧凑且可分，且不增加推理成本。在DIS5K基准上的评估表明，DDA在多种架构下均持续提升分割精度、边界锐度和模型置信度。结果表明，集成判别分析为构建更鲁棒的分割模型提供了一种简单有效的方法。
原文摘要 · Abstract (English)
Accurate image segmentation remains challenging, particularly in generating sharp, confident boundaries. While modern architectures have advanced the field, many of them still rely on standard loss functions like Cross-Entropy and Dice, which often neglect the discriminative structure of learned features, leading to inaccurate boundaries. This work introduces Deep Discriminant Analysis (DDA), a differentiable, architecture-agnostic loss function that embeds classical discriminant principles for network training. DDA explicitly maximizes between-class variance while minimizing within-class one, promoting compact and separable feature distributions without increasing inference cost. Evaluations on the DIS5K benchmark demonstrate that DDA consistently improves segmentation accuracy, boundary sharpness, and model confidence across various architectures. Our results show that integrating discriminant analysis offers a simple, effective path for building more robust segmentation models.
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