Explainable Attention for Few-shot Learning and Beyond
Bahareh Nikpour Narges Armanfard
Attention mechanisms have exhibited promising potential in enhancing learning
models by identifying salient portions of input data. This is particularly
valuable in scenarios where limited training samples are accessible due to
challenges in data collection and labeling. Drawing inspiration from human
recognition processes, we posit that an AI baseline's performance could be more
accurate and dependable if it is exposed to essential segments of raw data
rather than the entire input dataset, akin to human perception. However, the
task of selecting these informative data segments, referred to as hard
attention finding, presents a formidable challenge. In situations with few
training samples, existing studies struggle to locate such informative regions
due to the large number of training parameters that cannot be effectively
learned from the available limited samples. In this study, we introduce a novel
and practical framework for achieving explainable hard attention finding,
specifically tailored for few-shot learning scenarios, called FewXAT. Our
approach employs deep reinforcement learning to implement the concept of hard
attention, directly impacting raw input data and thus rendering the process
interpretable for human understanding. Through extensive experimentation across
various benchmark datasets, we demonstrate the efficacy of our proposed method.