Extracting Explainable Deep Representation for Machine Tutoring
In this work, we propose a machine tutoring framework through a modified Adversarial AutoEncoder(AAE) for Interactive Machine Learning(IML). In IML, masters can teach machines for valuable knowledge and skills and machines can transfer such knowledge and skills to novices. Generally in IML, we can d...
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| Veröffentlicht in: | 2019 IEEE International Conference on Big Data (Big Data) S. 4651 - 4658 |
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| Hauptverfasser: | , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
01.12.2019
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| Online-Zugang: | Volltext |
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| Zusammenfassung: | In this work, we propose a machine tutoring framework through a modified Adversarial AutoEncoder(AAE) for Interactive Machine Learning(IML). In IML, masters can teach machines for valuable knowledge and skills and machines can transfer such knowledge and skills to novices. Generally in IML, we can discuss transferring knowledge, something to be known and skills, something needs to know and to practice to learn it well from masters to machines and from machines to novices. In this work, we focus on one part of IML techniques, transferring knowledge and skills from machines to novices, called machine tutoring and realize it, in a Taiko-drum playing game platform. In Taiko-drum playing, skill learning is considered more important than knowledge learning. In particular, we adopt Internal Measurement Unit (IMU), attached on novice's forearms to collect the necessary data to understand novice's motions. We assume that the IMU data can help us understand how well novices learn on some particular motions from machines and from masters eventually. On the side, we capture game scores in each novice's play for the evaluation purpose. Four songs with different levels from easy to hard are used in the experiments. When a novice plays the game, we extract the explainable values from latent features in a deep learner for the representation of the novice's play. The machine tutoring is operated as we show the visualization that is built given the latent representation to the novice to suggest on how we may perform a task better and better as we play more. We demonstrate the effectiveness of the proposed machine tutoring framework by using three different deep neural network structures stacked on the Autoencoder included in the framework including MLP, CNN, and LSTM. The results show that AAE-regression CNN (AAER-CNN) performs the best on latent information representation when the song level is from easy to normal, and AAER-LSTM also performs the best when the song level is difficult on two tasks namely score prediction subject classification. Afterwards, we illustrate how the representation can help on machine tutoring. |
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| DOI: | 10.1109/BigData47090.2019.9006437 |