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)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Few-Shot Image Classification Mini-ImageNet-CUB 5-way (5-shot) Baseline++ (Chen et al., 2019) Accuracy 62.04 # 4

Results from Other Papers


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK SOURCE PAPER COMPARE
Few-Shot Image Classification Mini-ImageNet-CUB 5-way (1-shot) Baseline++ (Chen et al., 2019) Accuracy 33.04 # 9

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet