Computational predictions of mass spectra from molecules have enabled the discovery of clinically relevant metabolites.

The data is collected in Peking University and uses the same data format as SemanticKITTI. To ensure all tasks are well-defined, we formalize consistent and compatible semantic class vocabulary across the above datasets, ensuring there is a one-to-one mapping between all semantic classes. As for ASFDA and ADA settings, we have an additional warm-up stage, i.e., the network is Both source and target data have a batch size of 16. Both training loss and validation loss consistently decrease over time, indicating effective model training. We report mIoU results across existing AL approaches in Table A3.

Thus, these methods are limited by the delays caused by straggling workers.