self supervised model converging to a constant
I was trying to train a Barlow twins model for image classification. Nonetheless, I encountered a problem after finishing my model training. It seems that the model has become a constant always returning the number 2046 with a slightly variable decimal part no matter how different the two giving images are.
The model tries to minimize the cross correlation matrix to the identity matrix.
is there a way to overcome this problem.