[D] Lower batch size for sequential learning problem with class imbalance • r/MachineLearning

Does having a smaller batch size, perhaps even size 1, help in a sequential learning problem ( where there is an output corresponding to each input) where the dataset is imbalanced? Suppose my batch size is 4 and timesteps are 10. Class 2 rarely occurs in the dataset. In such cases, can the presence of multiple samples in minibatch adversarially impact training of each sample (especially those which contain label 2)? Since label 2 may not even occur in some samples of a minibatch, the network may not learn to predict 2 for samples in which it does occur.