which loss should I use

[yuzhiguo07]

Hi, thank you for your outstanding contributions!

I want to have a quick try using GJS on my own task, for 2-state and 3-state classification tasks, should I follow the hyper-parameters in C10/sym/js-40.yaml which using JSWCS loss and setting the js_weights to [0.7, 0.3]?

Also, I have another regression task using MAE loss originally, which loss should I use in losses.py to replace the MAE loss with?

Thank you very much.

[ErikEnglesson]

Hi, thank you for you interest in our work!

Classification
To use GJS with two predictive distributions from predicting on two strongly augmented versions of the original training image, please use:
loss: JSWCS
augs: ss
js_weights: 0.3 0.35 0.35

The JSWCS loss is used for both JS and GJS, where the number of characters for the augs parameter determines if you are using JS (1) or GJS (>=2). The parameters for js_weights corresponds to [\pi_1, \pi_2, \pi_3] in the paper. js_weights: 0.3 0.35 0.35 should be a good starting point, but the amount of label noise in the dataset determines the optimal setting for js_weights, which can be found through a hyper-parameter search.

Regression
Unfortunately, losses.py only contains loss functions for classification.

You could easily add your own regression loss to losses.py, but don't forget to add it to the get_criterion function and as a possible choice for the loss argument on line 454 in train.py.

Thanks again.

[yuzhiguo07]

Perfect! Thank you for your information!