A Closer Look at Few-shot Classification

ICLR 2019 Wei-Yu ChenYen-Cheng LiuZsolt KiraYu-Chiang Frank WangJia-Bin Huang

Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples. While significant progress has been made, the growing complexity of network designs, meta-learning algorithms, and differences in implementation details make a fair comparison difficult... (read more)

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