  Deep learning based discriminative methods, being the state-of-the-art
machine learning techniques, are ill-suited for learning from lower amounts of
data. In this paper, we propose a novel framework, called simultaneous two
sample learning (s2sL), to effectively learn the class discriminative
characteristics, even from very low amount of data. In s2sL, more than one
sample (here, two samples) are simultaneously considered to both, train and
test the classifier. We demonstrate our approach for speech/music
discrimination and emotion classification through experiments. Further, we also
show the effectiveness of s2sL approach for classification in low-resource
scenario, and for imbalanced data.
