Computer Vision MT25, Loss function design
Flashcards
@Define the smooth $L _ 1$ loss.
\[\text{smooth} _ {L _ 1}(x) = \begin{cases}
0.5x^2 &\text{if } \vert x \vert < 1 \\
\vert x \vert - 0.5 &\text{otherwise}
\end{cases}\]
The loss for R-CNN looks something like the following:
\[L(y, \hat P, b, \hat b) = -\log \hat P(y) + \lambda \mathbb I[y \ge 1] L _ \text{reg}(b, \hat b)\]
where:
- $y$ is the ground truth class of an object in ground truth bounding box $b$
- $\hat P$ are the predicted class probabilities and $\hat b$ is the predicted box
- $L _ \text{reg}$ is a loss for the bounding box regression
In what sense is this a multi-task loss?
You are combining two separate loss functions, one for the class prediction task and one for the bounding box regression task.