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SSIM和SSIM Loss 理解 #57

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creater-zq opened this issue Apr 27, 2021 · 0 comments

creater-zq commented Apr 27, 2021

通过图5，在训练前期，SSIM损失优先拟合前景和边界区域，会忽视背景的准确性，前景损失起关键作用，这有助于优化边界和前景。随着训练的进行，前景损失在降低，而背景的损失将会变为主要的损失。在训练的后期，BCE损失接近拟合(平坦)，而SSIM的背景损失依然存在，背景损失起主要作用，所以训练依然能够继续进行，让属于背景区域的预测值尽可能接近于0，图像更清晰。
非边界区域的SSIM更容易拟合为1，Loss拟合为0。
请问我上面的理解是否准确？如果不准确请指正，谢谢

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