Hey, I was recently doing a multi-label classification where there is a huge number of possible labels in the space of labels but each batch item will have only a few labels.
I tried optimizing my model using BCEWithLogitsLoss. But this didn’t turn out useful. My accuracy was close to negligible.
My optimization problem is such that I need a consistency guarantee with the predicted labels in the multi-class labels.
As a solution, I recently stumbled upon https://arxiv.org/abs/2009.14119 30
They have created a loss function which takes into account the imbalance of positive and negative labels of the target. My optimization which had utterly no growth inaccuracy after a lot of configurations started showing a lot of good promise because of this.
I have come around and seen too many posts on the performance of BCEWithLogits. So if you have a similar problem, Please try this out.