Skip to content

ServiceNow/am3

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

ServiceNow completed its acquisition of Element AI on January 8, 2021. All references to Element AI in the materials that are part of this project should refer to ServiceNow.

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,

About

Adaptive Cross-Modal Few-shot learning OSS code. This is a ServiceNow Research project that was started at Element AI.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages