negar-rostamzadeh / am3

Adaptive Cross-Modal Few-shot learning OSS code

ADAPTIVE CROSS-MODAL FEW-SHOT LEARNING (AW3)

Code for paper Adaptive Cross-Modal Few-shot Learning. [Arxiv]

Dependencies

• cv2
• numpy
• python 3.5+
• tensorflow 1.3+
• tqdm
• scipy

Datasets

First, designate a folder to be your data root:

export DATA_ROOT={DATA_ROOT}
Then, set up the datasets following the instructions in the subsections.

###miniImageNet

[Google Drive](1.05G)

# Download and place "mini-imagenet.zip" in "$DATA_ROOT/mini-imagenet".
mkdir -p $DATA_ROOT/mini-imagenet
cd $DATA_ROOT/mini-imagenet
mv ~/Downloads/mini-imagenet.zip .
unzip mini-imagenet.zip
rm -f mini-imagenet.zip

###tieredImageNet [Google Drive](14.33G)

# Download and place "tiered-imagenet.zip" in "$DATA_ROOT/tiered-imagenet".
mkdir -p $DATA_ROOT/tiered-imagenet
cd $DATA_ROOT/tiered-imagenet
mv ~/Downloads/tiered-imagenet.tar.gz .
tar -xvf tiered-imagenet.tar.gz
rm -f tiered-imagenet.tar.gz

AM3-ProtoNet

1-shot experiments

For mini-ImageNet:

python AM3_protonet++.py --data_dir $DATA_ROOT/mini-imagenet/ 
--num_tasks_per_batch 5 --num_shots_train 1 --num_shots_test 1 --train_batch_size 24 
--mlp_dropout 0.7 --att_input word --task_encoder self_att_mlp 
--mlp_type non-linear --mlp_weight_decay 0.001
--log_dir $EXP_DIR

For tiered-ImageNet:

python AM3_protonet++.py --data_dir $DATA_ROOT/tiered-imagenet/ 
--num_tasks_per_batch 5 --num_shots_train 1 --num_shots_test 1 --train_batch_size 24
--num_steps_decay_pwc 10000 --number_of_steps 80000  
--mlp_dropout 0.7 --att_input word --task_encoder self_att_mlp 
--mlp_type non-linear --mlp_weight_decay 0.001
--log_dir $EXP_DIR

5-shot experiments

For mini-ImageNet:

python AM3_protonet++.py --data_dir $DATA_ROOT/mini-imagenet/  
--mlp_dropout 0.7 --att_input word --task_encoder self_att_mlp 
--mlp_type non-linear --mlp_weight_decay 0.001
--log_dir $EXP_DIR

For tiered-ImageNet:

python AM3_protonet++.py --data_dir $DATA_ROOT/tiered-imagenet/ 
--num_steps_decay_pwc 10000 --number_of_steps 80000 
--mlp_dropout 0.7 --att_input word --task_encoder self_att_mlp 
--mlp_type non-linear --mlp_weight_decay 0.001
--log_dir $EXP_DIR

##AM3-TADAM Note that you may need to tune "--metric_multiplier_init" which is a TADAM hyper-parameter, via cross-validation to achieve sota results. The range of "--metric_multiplier_init" is usually (5, 10).

1-shot experiments

For mini-ImageNet:

python AM3_TADAM.py --data_dir $DATA_ROOT/mini-imagenet/ 
--num_tasks_per_batch 5 --num_shots_train 1 --num_shots_test 1 --train_batch_size 24 --metric_multiplier_init 5
--feat_extract_pretrain multitask --encoder_classifier_link cbn --num_cases_test 100000 
--activation_mlp relu --att_dropout 0.7 --att_type non-linear --att_weight_decay 0.001 
--mlp_dropout 0.7 --mlp_type non-linear --mlp_weight_decay 0.001 --att_input word --task_encoder self_att_mlp 
--log_dir $EXP_DIR

For tiered-ImageNet:

python AM3_TADAM.py --data_dir $DATA_ROOT/tiered-imagenet/ 
--num_tasks_per_batch 5 --num_shots_train 1 --num_shots_test 1 --train_batch_size 24 --metric_multiplier_init 5
--feat_extract_pretrain multitask --encoder_classifier_link cbn --num_steps_decay_pwc 10000 
--number_of_steps 80000 --num_cases_test 100000 --num_classes_pretrain 351 
--att_dropout 0.9  --mlp_dropout 0.9 
--log_dir "$EXP_DIR

5-shot experiments

For mini-ImageNet:

python AM3_TADAM.py --data_dir $DATA_ROOT/mini-imagenet/ 
--metric_multiplier_init 7
--feat_extract_pretrain multitask --encoder_classifier_link cbn --num_cases_test 100000 
--activation_mlp relu --att_dropout 0.7 --att_type non-linear --att_weight_decay 0.001 
--mlp_dropout 0.7 --mlp_type non-linear --mlp_weight_decay 0.001 --att_input word --task_encoder self_att_mlp 
--log_dir $EXP_DIR

For tiered-ImageNet:

python AM3_TADAM.py --data_dir $DATA_ROOT/tiered-imagenet/ 
--metric_multiplier_init 7
--feat_extract_pretrain multitask --encoder_classifier_link cbn --num_steps_decay_pwc 10000 
--number_of_steps 80000 --num_cases_test 100000 --num_classes_pretrain 351 
--att_dropout 0.9  --mlp_dropout 0.9 
--log_dir "$EXP_DIR

Citation

If you use our code, please consider cite the following:

• Chen Xing,