I'm comparing two models, and want to clarify the weird results.
Model 1 achieves lower training loss than model 2, but get higher validation loss.
Because over-fitting and under-fitting are determined by comparing training/validation loss of themselves, therefore, I think it's not an issue of over-fitting.
Precisely, I'm now training with point cloud classification tasks,
got model 1 training loss : 1.51, test loss : 1.56 / model 2 training loss : 1.37, test loss : 1.58.
All other conditions are the same.
So the question is, how can this happens, test loss is lower than training loss?
it will be grateful anyone can help our problems.