Tamar Ben David

Paper 2024/419 , Ben Gurion University , Ben Gurion University , Ariel University , Ariel University , Ariel University Abstract Evolving secret-sharing schemes, defined by Komargodski, Naor, and Yogev [TCC 2016B, IEEE Trans. on Info. Theory 2018], are secret-sharing schemes in which there is no a-priory bound on the number of parties. In such schemes, parties arrive one by one; when a party arrives, the dealer gives it a share and cannot update this share in later stages.

Occasionally when we create a dataset for training, we don’t have enough samples for all the classes. Sometimes we have many classes with a low amount of samples (long tail) so the model won’t be able to classify those classes well. Even if we train the model several times or try different models we still get the same poor results - because the problem here is that we don’t have enough examples from those classes. How can we collect more data from those specific classes?