Example-based Methods to Explain the Internal Generative Mechanism of Deep Generative Neural Networks

Department of Mechanical Engineering (System Design and Control Engineering)"This thesis studies the internal generative mechanism of Deep Generative Neural Networks based on the example-based method. Since the adversarial training scheme which the generator and the discriminator compete for each objective has emerged, deep generative neural networks with this scheme (mainly called Generative Adversarial Networks, GANs) have shown the remarkable generation performance in various fields (e.g., image or time-series generations.). However, despite of the advances with various structures of the network and training strategies, the state-of-art generative models still generate the low visual fidelity of outputs called artifact. In particular, because the previous researches mainly focus to increase the overall performance of the generator, the internal generative mechanism for artifacts is not fully analyzed yet. In this thesis, the example-based methods are provided to understand the internal generative mechanism of deep generative neural networks. Especially, we mainly focus on artifacts of GANs. The aforementioned problems are important for the reliability of the generator. To exploit the generator for real-world applications, the quality of the generations should be guaranteed. In particular, for the mission-critical system related to human, the low visual fidelity of generations may cause the fatal problems. At the same time, the described problems are challenging: (1) considering the internal generative mechanism of deep generative neural networks indicates analyzing the internal units (or neurons) of internal layers of the generator. Because the modern networks not only use the high dimensional latent space but also numerous internal neurons to generate output, it is non-trivial to interpret internal components directly, and it will be in-efficient. (2) highly non-linear nature of Deep Neural Network disturbs the intuitive interpretation of the functionality of the generator. (3) the supervision (e.g., pre-trained segmentation network) can be needed to understand the mechanism of the internal components. However, because the additional networks aligned with the generation modes of the generator are not always available, the dependency for extra resources will reduce practicality for analysis. The goal of this thesis is to devise tools for the given problems. The first contribution of this thesis is to devise the efficient sampling strategy which uses the generative boundaries to understand shared semantic information in the internal layers of the generator. Second, this thesis presents the classifier-based internal unit identification to detect internal defective units automatically which cause the low visual fidelity of generations. Furthermore, the sequential correction method is proposed based on the detected units to remove the areas of the low visual fidelity as maintaining the plausible areas. Last but not least, to alleviate the weakness for the supervised analysis, this thesis investigate the internal properties of neurons to understand artifact generating internal units of Generative Adversarial Networks in an unsupervised manner. This thesis also provides the partial geometrical analysis for observed properties based on the relationship between generator and discriminator. "ope

An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks

Deep generative neural networks (DGNNs) have achieved realistic and high-quality data generation. In particular, the adversarial training scheme has been applied to many DGNNs and has exhibited powerful performance. Despite of recent advances in generative networks, identifying the image generation mechanism still remains challenging. In this paper, we present an explorative sampling algorithm to analyze generation mechanism of DGNNs. Our method efficiently obtains samples with identical attributes from a query image in a perspective of the trained model. We define generative boundaries which determine the activation of nodes in the internal layer and probe inside the model with this information. To handle a large number of boundaries, we obtain the essential set of boundaries using optimization. By gathering samples within the region surrounded by generative boundaries, we can empirically reveal the characteristics of the internal layers of DGNNs. We also demonstrate that our algorithm can find more homogeneous, the model specific samples compared to the variations of {\epsilon}-based sampling method.Comment: AAAI 202

인공지능, 기계공학에서 이렇게 쓰인다

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Vision-based real-time layer error quantification for additive manufacturing

Quality assurance of Additive Manufacturing (AM) products has become an important issue as the AM technology is extending its application throughout the industry. However, with no definite measure to quantify the error of the product and monitor the manufacturing process, many attempts are made to propose an effective monitoring system for the quality assurance of AM products. In this research, a novel approach for quantifying the error in real-time is presented through a closed-loop vision-based tracking method. As conventional AM processes are open-loop processes, we focus on the implementation of real-time error quantification of the products through the utilization of a closed-loop process. Three test models are designed for the experiment, and the tracking data from the camera will be compared with the G-code of the product to evaluate the geometrical errors. The results obtained from the camera analysis will then be validated through comparison of the results obtained from a 3D scanner

Wavelet-like convolutional neural network structure for time-series data classification

Time-series data often contain one of the most valuable pieces of information in many fields including manufacturing. Because time-series data are relatively cheap to acquire, they (e.g., vibration signals) have become a crucial part of big data even in manufacturing shop floors. Recently, deep-learning models have shown state-of-art performance for analyzing big data because of their sophisticated structures and considerable computational power. Traditional models for a machinery-monitoring system have highly relied on features selected by human experts. In addition, the representational power of such models fails as the data distribution becomes complicated. On the other hand, deep-learning models automatically select highly abstracted features during the optimization process, and their representational power is better than that of traditional neural network models. However, the applicability of deep-learning models to the field of prognostics and health management (PHM) has not been well investigated yet. This study integrates the "residual fitting" mechanism inherently embedded in the wavelet transform into the convolutional neural network deep-learning structure. As a result, the architecture combines a signal smoother and classification procedures into a single model. Validation results from rotor vibration data demonstrate that our model outperforms all other off-the-shelf feature-based models

Fault detection and identification method using observer-based residuals

Manufacturing machinery is becoming increasingly complicated, and machinery breakdowns not only reduce efficiency, but also pose safety hazards. Due to the needs for maintaining high reliability within facility operation, various methods for condition monitoring are suggested as the importance of maintenance has increased. Among the various prognostics and health management (PHM) techniques, this paper introduces a model-based fault detection and isolation (FDI) technique for the diagnosis of machine health conditions. The proposed approach identifies faults by extracting fault signal information such as the magnitude or shape of the fault based on a defined relationship between a fault signal and observer theory. To validate the proposed method, a numerical simulation is conducted to demonstrate its fault detection and identification capabilities in various situations. The proposed method and data-driven methods are then compared with regard to their fault diagnosis performance. (C) 2018 Elsevier Ltd. All rights reserved

Deep Learning based Diagnostics of Orbit Patterns in rotating machinery

Vibration-based orbit analysis has been employed as a powerful tool in diagnosing the operating state for rotating machinery in power plants. However, due to the difficulties of extracting mathematical features for data-driven approaches in the orbit analysis, it heavily depends on the expert knowledge or experience. In this paper, the deep learning algorithm in machine learning is used to develop autonomous orbit pattern recognition. In details, the convolutional neural network is implemented to build up weights between convolution kernels and pixels, and to construct the entire structure of the neural networks. Finally, the trained network enables us to classify the shapes of the orbit via orbit shape images and its result can estimate fault modes of the rotating machinery. The proposed framework is demonstrated with a rotating testbed