  In Multiple Instance learning (MIL), weak labels are provided at the bag
level with only presence/absence information known. However, there is a
considerable gap in performance in comparison to a fully supervised model,
limiting the practical applicability of MIL approaches. Thus, this paper
introduces a novel semi-weak label learning paradigm as a middle ground to
mitigate the problem. We define semi-weak label data as data where we know the
presence or absence of a given class and the exact count of each class as
opposed to knowing the label proportions. We then propose a two-stage framework
to address the problem of learning from semi-weak labels. It leverages the fact
that counting information is non-negative and discrete. Experiments are
conducted on generated samples from CIFAR-10. We compare our model with a
fully-supervised setting baseline, a weakly-supervised setting baseline and
learning from pro-portion (LLP) baseline. Our framework not only outperforms
both baseline models for MIL-based weakly super-vised setting and learning from
proportion setting, but also gives comparable results compared to the fully
supervised model. Further, we conduct thorough ablation studies to analyze
across datasets and variation with batch size, losses architectural changes,
bag size and regularization
