About labeled Regression Loss(Gaussian Probability)
JooYJang opened this issue · 0 comments
JooYJang commented
Hello, Thank you for sharing interesting research
I'm wondering why gaussian pdf is directly used to formulate labeled regression loss as a cross-entropy loss.
Is this generally used?
gaussian = gaussian_dist_pdf(fg_pred_deltas[..., :4], gt_pred_deltas, sigma_xywh)
loss_box_reg_gaussian = - torch.log(gaussian + 1e-9).sum()
and gaussian_dist_pdf
def gaussian_dist_pdf(val, mean, var, eps=1e-9):
simga_constant = 0.3
return torch.exp(-(val - mean) ** 2.0 / (var + eps) / 2.0) / torch.sqrt(2.0 * np.pi * (var + simga_constant))
As far as I understand, the goal of mean and variance term used in this paper is to be gt_box (x_off, y_off, box_width, box_height) and zeros, since gt has direct delta fucntion with mean = gt_box and var = 0.
In this respect, directly using gaussian pdf as above would have infinite loss.
p.s/ How did you get sigma_constant = 0.3??
Thanks in advance
Joo Young Jang