3 ms·
Most dCNN classifiers I’ve seen used simply rely on softmax to provide a “probability” among the classes. But it really is a score that has been normalized acro
by lokimedes 9y ago
Most dCNN classifiers I’ve seen used simply rely on softmax to provide a “probability” among the classes. But it really is a score that has been normalized across the classes. Having 0.7 of one class does not mean the same level of discrimination from the remaining classes as it does for another class with the same score. By only using the maximum scoring class you don’t account for what score value is sufficient to claim a significant discrimination between the maximum score and the alternatives.
- deleted 9y ago[deleted]
- nonbel 9y agoI see how the probability (softmax output) contains more info than simply the class with highest probability, but not what you want regarding "significant discrimination". Perhaps you want to weight different types of errors differently? Eg: https://github.com/Hezi-Resheff/paper-log-bilinear-loss https://github.com/Hezi-Resheff/paper-log-bilinear-loss