This is the TensorFlow implementation for the paper "Cost-Effective Interactive Attention Learning with Neural Attention Processes (ICML 2020) : https://arxiv.org/abs/2006.05419
We propose a novel interactive learning framework which we refer to as Interactive Attention Learning (IAL), in which the human supervisors interactively manipulate the allocated attentions, to correct the model's behaviour by updating the attention-generating network. However, such a model is prone to overfitting due to scarcity of human annotations, and requires costly retraining. Moreover, it is almost infeasible for the human annotators to examine attentions on tons of instances and features. We tackle these challenges by proposing a sample-efficient attention mechanism and a cost-effective reranking algorithm for instances and features. First, we propose Neural Attention Processes (NAPs), which is an attention generator that can update its behaviour by incorporating new attention-level supervisions without any retraining. Secondly, we propose an algorithm which prioritizes the instances and the features by their negative impacts, such that the model can yield large improvements with minimal human feedback. We validate IAL on various time-series datasets from multiple domains (healthcare, real-estate, and computer vision) on which it significantly outperforms baselines with conventional attention mechanisms, or without cost-effective reranking, with substantially less retraining and human-model interaction cost.
Contribution of this work
- We propose a novel interactive learning framework which iteratively updates the model by interacting with the human supervisor via the generated attentions.
- To minimize the retraining cost, we propose a novel probabilistic attention mechanism which sampleefficiently incorporates new attention-level supervisions on-the-fly without retraining and overfitting.
- To minimize human supervision cost, we propose an efficient instance and feature reranking algorithm, that prioritizes them based on their negative impacts on the prediction, measured either by uncertainty, influence function, or counterfactual estimation.
- We validate our model on five real-world datasets with binary, multi-label classification, and regression tasks, and show that our model obtains significant improvements over baselines with substantially less retraining and human feedback cost.
Structure of Neural Attention Processes (NAP)
- NAP can incorporate new labeled instances into the context to immediately change its attention-generating behaviour without retraining via amortization, which also allows the annotator to see the effect of his/her annotation on the prediction on the fly.
Structure of Cost-Effective Reranking Algorithm (CER)
- CER prioritizes them based on their negative impacts on the prediction, measured either by uncertainty, influence function, or counterfactual estimation.
- Python 3.5
- Tensorflow 1.14.0
- CUDA 10.0
- cudnn 7.6.5
Install the python packages:
$ pip install flask
$ pip install opencv-python
$ pip install pyqt5
Go to the folder of each dataset (i.e. data/EHR, data/Finance, or data/Squat) to check. All datasets have been already preprocessed.
Risk Prediction Tasks (Heart Failure/Cerebral Infarction/CardioVascular disease)
Cerebral Infarction
- Initial Round & Execute uncertainty-counterfactual combination
$ python train.py
- Online Annotation Interface (Flask)
$ python ./hil_medical_annotator/main.py
- Further Round (Retraining)
$ python retrain.py
- Online Annotation Interface (Flask)
$ python ./hil_medical_annotator/main.py
- Initial Round Sampling
$ cd ./hil_squat_annotator/model
$ python Sampling.py
- Online Annotation Interface (PyQt5)
$ cd ..
$ python hil_keypoint.py
The results in the main paper (Final AUC over five training rounds):
| Heart Failure | Cerebral Infartion | CardioVascular Disease (CVD) | |
|---|---|---|---|
| Random-UA | 0.6231 ± 0.03 | 0.6491 ± 0.01 | 0.6112 ± 0.02 |
| Random-NAP | 0.6414 ± 0.01 | 0.6674 ± 0.02 | 0.6284 ± 0.01 |
| AILA | 0.6363 ± 0.03 | 0.6602 ± 0.03 | 0.6193 ± 0.02 |
| IAL-NAP | 0.6612 ± 0.02 | 0.6892 ± 0.03 | 0.6371 ± 0.02 |
IAL-NAP Combinations
| Instance-level | Feature-level | Heart Failure | Cerebral Infartion | CardioVascular Disease |
|---|---|---|---|---|
| Influence Function | Uncertainty | 0.6563 ± 0.01 | 0.6821 ± 0.02 | 0.6308 ± 0.02 |
| Influence Function | Influence Function | 0.6514 ± 0.02 | 0.6825 ± 0.01 | 0.6329 ± 0.03 |
| Influence Function | Counterfactual | 0.6592 ± 0.02 | 0.6921 ± 0.03 | 0.6379 ± 0.02 |
| Uncertainty | Counterfactual | 0.6612 ± 0.01 | 0.6892 ± 0.03 | 0.6371 ± 0.02 |
| Mean-Percentage error | |
|---|---|
| Random-UA | 0.2222 ± 0.04 |
| Random-NAP | 0.2061 ± 0.01 |
| AILA | 0.2119 ± 0.01 |
| IAL-NAP | 0.1835 ± 0.02 |
IAL-NAP Combinations
| Instance-level | Feature-level | Mean-Percentage error |
|---|---|---|
| Influence Function | Uncertainty | 0.1921 ± 0.01 |
| Influence Function | Influence Function | 0.1865 ± 0.02 |
| Influence Function | Counterfactual | 0.1863 ± 0.02 |
| Uncertainty | Counterfactual | 0.1835 ± 0.01 |
| Mean Accuracy | |
|---|---|
| Random-UA | 0.8521 ± 0.02 |
| Random-NAP | 0.8525 ± 0.01 |
| AILA | 0.8425 ± 0.01 |
| IAL-NAP | 0.8689 ± 0.01 |
IAL-NAP Combinations
| Instance-level | Feature-level | Mean Accuracy |
|---|---|---|
| Influence Function | Uncertainty | 0.8712 ± 0.01 |
| Influence Function | Influence Function | 0.8632 ± 0.01 |
| Influence Function | Counterfactual | 0.8682 ± 0.01 |
| Uncertainty | Counterfactual | 0.8689 ± 0.02 |
*** Retraining time ***
- Heart Failure
s=1 s=2 s=3 s=4
Random-UA 31.1532s 34.5223s 39.2324s 38.2094s
AILA 43.2324s 42.2102s 45.4364s 47.1129s
Random-NAP 9.2445s 8.2309s 9.2320s 9.1083s
IAL-NAP 9.2309s 8.3693s 9.1129s 9.0324s
- Cerebral Infarction
s=1 s=2 s=3 s=4
Random-UA 63.3984s 50.3209s 49.1896s 50.9103s
AILA 49.8931s 45.2804s 60.0425s 58.3929s
Random-NAP 22.5792s 18.4052s 18.9384s 17.9374s
IAL-NAP 18.3982s 19.1423s 18.7834s 16.8199s
- CardioVascular Disease (CVD)
s=1 s=2 s=3 s=4
Random-UA 74.2351s 75.5424s 77.9324s 78.2088s
AILA 46.2524s 47.2396s 69.2441s 73.2692s
Random-NAP 21.7324s 27.5324s 31.6341s 28.2392s
IAL-NAP 29.8324s 28.2334s 29.7326s 27.2044s
- Real Estate Forecasting
s=1 s=2 s=3 s=4
Random-UA 239.2379s 236.5408s 237.9478s 239.2818s
AILA 228.2123s 261.5464s 241.9364s 162.7389s
Random-NAP 148.7354s 163.5324s 169.1341s 162.3813s
IAL-NAP 164.8328s 163.1334s 147.5326s 150.9381s
- Squat Posture
s=1 s=2 s=3 s=4
Random-UA 32.1194s 27.0938s 32.9482s 32.2984s
AILA 25.8931s 24.9374s 24.1850s 23.3081s
Random-NAP 7.2324s 8.9034s 8.2984s 8.9374s
IAL-NAP 7.2183s 7.2314s 6.1254s 8.2109s
*** Mean Response Time (mean-RT) ***
- Heart Failure
s=1 s=2 s=3 s=4
Random-NAP 204.2236s 198.9244s 179.8526s 174.4985s
IAL-NAP 155.9324s 150.8924s 131.9224s 139.9074s
- Cerebral Infarction
s=1 s=2 s=3 s=4
Random-NAP 184.2939s 182.5246s 189.5029s 179.8127s
IAL-NAP 141.2843s 128.8344s 132.5524s 129.3053s
- CardioVascular Disease (CVD)
s=1 s=2 s=3 s=4
Random-NAP 250.3955s 239.8921s 226.2995s 231.4734s
IAL-NAP 192.2392s 173.5641s 171.3423s 165.9254s
- Real Estate Forecasting
s=1 s=2 s=3 s=4
Random-NAP 377.6324s 319.8921s 316.2O95s 289.5034s
IAL-NAP 299.2941s 251.5634s 243.3423s 240.8254s
- Squat Posture
s=1 s=2 s=3 s=4
Random-NAP 124.2324s 131.2324s 128.2324s 114.2324s
IAL-NAP 96.2973s 81.7391s 80.2393s 78.2924s
| Feature | Meaning |
|---|---|
| Age | Age |
| HDL | High-densitylipoproteins holesterol |
| Smoking | Whether currently smokes a cigarette |
| SysBP | Systolic blood pressure |
| LDL | Low-density lipoprotein cholesterol. |
- Visualization of attentions for a selected patient on CardioVascular Disease (CVD) prediction task. Contribution indicates the extent to which each individual feature affects the onset of CVD in 1 year.
If you found the provided code useful, please cite our work.
@inproceedings{heo2020cost,
title={Cost-Effective Interactive Attention Learning with Neural Attention Processes},
author={Jay Heo and Junhyeon Park and Hyewon Jeong and Kwang Joon Kim, and Juho Lee and Eunho Yang and Sung Ju Hwang},
booktitle={ICML},
year={2020}
}