kjunelee/MetaOptNet

Question about meta-validation and meta-testing

LoveMiki opened this issue · 4 comments

LoveMiki commented

Thanks for sharing the codes.
When i am reading your codes, i find that you use 'CIFAR_FS_train.pickle' as the base categories in validation stage and testing stage. According to my understanding, the support set and query set in validation stage should be constructed only from the 'CIFAR_FS_val.pickle'.
Why did you use 'CIFAR_FS_train.pickle' as the base categories(support set) and 'CIFAR_FS_val.pickle' as the novel categories(query set) in the validation stage?

kjunelee commented

I think you're referring to file_train_categories_val_phase and file_train_categories_test_phase. Actually, they are not used during meta-validation and meta-testing stages.

LoveMiki commented

I think you're referring to file_train_categories_val_phase and file_train_categories_test_phase. Actually, they are not used during meta-validation and meta-testing stages.

Thanks for your reply! When i was reading the codes of MetaOptNetHead_SVM_CS, I feel confused by how you constructed the parameter matrices (i.e., G,e,C,h,A,b). Could you give some guide instructions of how to construct these matrices?

kjunelee commented

In essence, we follow Crammer and Singer's multi-class SVM implementation in the following paper: On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines (Crammer and Singer, Journal of Machine Learning Research 2001).
The original paper of Crammer&Singer does not provide efficient & vectorized formulation. Thus, it required a non-trivial amount of research to develop an efficient matrix implementation. For example, the following paper provides one nice way to implement Crammer & Singer: A Comparison of Methods for Multi-class Support Vector Machines.

LoveMiki commented

In essence, we follow Crammer and Singer's multi-class SVM implementation in the following paper: On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines (Crammer and Singer, Journal of Machine Learning Research 2001).
The original paper of Crammer&Singer does not provide efficient & vectorized formulation. Thus, it required a non-trivial amount of research to develop an efficient matrix implementation. For example, the following paper provides one nice way to implement Crammer & Singer: A Comparison of Methods for Multi-class Support Vector Machines.

Thank you so much for your immediate reply! These information is very helpful.