TAFSSL
Task-Adaptive Feature Sub-Space Learning for few-shot classification
https://arxiv.org/abs/2003.06670
Code for the experiments
(according to tables and figures in the paper):
Table 1: Transductive setting
python exp_table.py
Table 2: Semi supervised setting
python exp_semi.py
Figure 2: Number of queries in transductive FSL setting
python exp_num_query.py
Figure 3: The affect of the unlabeled data noise on the performance
python exp_noise_semi.py
Figure 4: ICA dimension vs accuracy
python exp_projection_dim.py
Figure 5: Unbalanced
python exp_unbalanced.py
To re-create the feature files:
1. Download miniImageNet / tieredImageNet
2. Generate splits:
python src/utils/tieredImagenet.py --data path-to-tiered --split split/tiered/
3. Pre-train a model and store the features:
python ./src/train.py -c $<$path to config file$>$
python ./src/train.py -c $<$path to config file$>$ --save-features --enlarge
License
Copyright 2019 IBM Corp. This repository is released under the Apachi-2.0 license (see the LICENSE file for details)