基于FCN的图像分割训练中修改真值标签是否违反机器学习规则？

Hey there, great question—let's unpack this clearly, because this kind of iterative, feature-driven approach is actually super common in modern segmentation research, and it doesn't violate any core ML/DL rules.

First: Modifying ground truth masks using FCN's argmax output #

This boils down to when you're modifying the ground truth, and how you're doing it:

• Offline preprocessing (safe and widely used): If you first train a baseline FCN, use its output mask to generate metrics like connected component counts, then adjust your original ground truth (e.g., mark small, likely noisy connected blocks as ignore labels) before retraining—this is a standard way to refine labels, especially in scenarios where your original annotations are imperfect or noisy. It's a form of weak supervision/label cleaning, and totally valid.
• Dynamic in-training modification (possible, with safeguards): If you're adjusting ground truth on the fly during training (e.g., each epoch, use the current model's output to tweak labels), this falls into self-training/iterative label refinement territory. It's not "against the rules," but you need to guard against error propagation: only use high-confidence model outputs (e.g., mask regions with prediction confidence > 0.9) to modify labels, and avoid overwriting regions where the model is uncertain. Many state-of-the-art semi-supervised segmentation methods use exactly this kind of loop.

Second: Adding RPN-like feature extraction branches #

This is even more straightforward—Faster R-CNN's RPN is a classic example of reusing backbone features for auxiliary tasks to boost main task performance. For your segmentation work, extracting info like candidate regions or connected components from FCN features (including the final score layer) is just adding an auxiliary branch to your model. This can help the main segmentation head learn better spatial or structural cues (e.g., prioritizing connected regions), and it's a standard technique in multi-task learning for segmentation.

Key things to watch out for #

• Avoid data leakage: If you're modifying labels dynamically, never use any test set data or metrics to adjust training labels—all operations must stay within your training fold.
• Control noise: Model outputs aren't perfect, especially early in training. Add thresholds or filters (like only keeping connected components above a certain size/confidence) to avoid injecting bad information into your ground truth.
• Validate empirically: Always run ablations—compare model performance with and without your modifications to make sure your changes are actually helping, not hurting, segmentation accuracy.

At the end of the day, this kind of approach is not just allowed—it's actively explored in research to tackle noisy labels, improve structural consistency, or boost semi-supervised performance. Go ahead and experiment with it!

内容的提问来源于stack exchange，提问作者Alex