Reviews: Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition

Originality: 7 / 10 This is a novel paper that is well motivated and executed. Admittedly, all of its components are not novel alone -- grid linear mixture for image augmentation [6], meta-learned generator [35], episodical procedure, and standard few-shot classifiers. The proposed pipeline itself is new and does provide insight that end-to-end image-augmentation is feasible with a strong generator initialization. And also, finetuning a GAN towards certain modalities (or observations) are not informatively studied before. Figure 1 and its experiments could serve as a good reference to researchers who want to study image augmentation.