Title: IMAGE DEFORMATION META-NETWORK FOR ONE-SHOT LEARNING. Abstract: Humans can robustly learn novel visual concepts even when images undergo various deformations and loose certain information. Incorporating this ability to synthesize deformed instances of new concepts might help visual recognition systems perform better one-shot learning, i.e., learning concepts from one or few examples. Our key insight is that, while the deformed images might not be visually realistic, they still maintain critical semantic information and contribute significantly in formulating classifier decision boundaries. Inspired by the recent progress on meta-learning, we combine a meta-learner with an image deformation network that produces additional training examples, and optimize both models in an endto- end manner. The deformation network learns to synthesize images by fusing a pair of images—a probe image that keeps the visual content and a gallery image that diversifies the deformations. We demonstrate results on the widely used oneshot learning benchmarks (miniImageNet and ImageNet 1K challenge datasets), which significantly outperform the previous state-of-the-art approaches.