Cross-Modulation Networks for Few-Shot Learning
This addresses the problem of few-shot learning for AI systems by introducing an incremental improvement in interaction mechanisms.
The paper tackles few-shot learning by proposing Cross-Modulation Networks, which enable support and query examples to interact throughout feature extraction via modulation, closing the gap with state-of-the-art on miniImageNet in the 5-way, 1-shot setting.
A family of recent successful approaches to few-shot learning relies on learning an embedding space in which predictions are made by computing similarities between examples. This corresponds to combining information between support and query examples at a very late stage of the prediction pipeline. Inspired by this observation, we hypothesize that there may be benefits to combining the information at various levels of abstraction along the pipeline. We present an architecture called Cross-Modulation Networks which allows support and query examples to interact throughout the feature extraction process via a feature-wise modulation mechanism. We adapt the Matching Networks architecture to take advantage of these interactions and show encouraging initial results on miniImageNet in the 5-way, 1-shot setting, where we close the gap with state-of-the-art.