Towards exploring adversarial learning for anomaly detection in complex driving scenes

One of the many Autonomous Systems (ASs), such as autonomous driving cars, performs various safety-critical functions. Many of these autonomous systems take advantage of Artificial Intelligence (AI) techniques to perceive their environment. But these perceiving components could not be formally verified, since, the accuracy of such AI-based components has a high dependency on the quality of training data. So Machine learning (ML) based anomaly detection, a technique to identify data that does not belong to the training data could be used as a safety measuring indicator during the development and operational time of such AI-based components. Adversarial learning, a sub-field of machine learning has proven its ability to detect anomalies in images and videos with impressive results on simple data sets. Therefore, in this work, we investigate and provide insight into the performance of such techniques on a highly complex driving scenes dataset called Berkeley DeepDrive.Comment: 2

Towards exploring adversarial learning for anomaly detection in complex driving scenes

One of the many Autonomous Systems (ASs), such as autonomous driving cars, performs various safety-critical functions. Manyof these autonomous systems take advantage of Artificial Intelligence (AI) techniques to perceive their environment. But these perceiving components could not be formally verified, since, the accuracy of such AI-based components has a high dependency on the quality of training data. So Machine learning (ML) based anomaly detection, a technique to identify data that does not belong to the training data could be used as a safety measuring indicator during the development and operational time of such AI-based components. Adversarial learning, a sub-field of machine learning has proven its ability to detect anomalies in images and videos with impressive results on simple data sets. Therefore, in this work, we investigate and provide insight into the performance of such techniques on a highly complex driving scenes dataset called Berkeley DeepDrive

Learning Representations for Novelty and Anomaly Detection

The problem of novelty or anomaly detection refers to the ability to automatically identify data samples that differ from a notion of normality. Techniques that address this problem are necessary in many applications, like in medical diagnosis, autonomous driving, fraud detection, or cyber-attack detection, just to mention a few. The problem is inherently challenging because of the openness of the space of distributions that characterize novelty or outlier data points. This is often matched with the inability to adequately represent such distributions due to the lack of representative data. In this dissertation we address the challenge above by making several contributions. (a)We introduce an unsupervised framework for novelty detection, which is based on deep learning techniques, and which does not require labeled data representing the distribution of outliers. (b) The framework is general and based on first principles by detecting anomalies via computing their probabilities according to the distribution representing normality. (c) The framework can handle high-dimensional data such as images, by performing a non-linear dimensionality reduction of the input space into an isometric lower-dimensional space, leading to a computationally efficient method. (d) The framework is guarded from the potential inclusion of distributions of outliers into the distribution of normality by favoring that only inlier data can be well represented by the model. (e) The methods are evaluated extensively on multiple computer vision benchmark datasets, where it is shown that they compare favorably with the state of the art

Representation Learning with Adversarial Latent Autoencoders

A large number of deep learning methods applied to computer vision problems require encoder-decoder maps. These methods include, but are not limited to, self-representation learning, generalization, few-shot learning, and novelty detection. Encoder-decoder maps are also useful for photo manipulation, photo editing, superresolution, etc. Encoder-decoder maps are typically learned using autoencoder networks.Traditionally, autoencoder reciprocity is achieved in the image-space using pixel-wisesimilarity loss, which has a widely known flaw of producing non-realistic reconstructions. This flaw is typical for the Variational Autoencoder (VAE) family and is not only limited to pixel-wise similarity losses, but is common to all methods relying upon the explicit maximum likelihood training paradigm, as opposed to an implicit one. Likelihood maximization, coupled with poor decoder distribution leads to poor or blurry reconstructions at best. Generative Adversarial Networks (GANs) on the other hand, perform an implicit maximization of the likelihood by solving a minimax game, thus bypassing the issues derived from the explicit maximization. This provides GAN architectures with remarkable generative power, enabling the generation of high-resolution images of humans, which are indistinguishable from real photos to the naked eye. However, GAN architectures lack inference capabilities, which makes them unsuitable for training encoder-decoder maps, effectively limiting their application space.We introduce an autoencoder architecture that (a) is free from the consequences ofmaximizing the likelihood directly, (b) produces reconstructions competitive in quality with state-of-the-art GAN architectures, and (c) allows learning disentangled representations, which makes it useful in a variety of problems. We show that the proposed architecture and training paradigm significantly improves the state-of-the-art in novelty and anomaly detection methods, it enables novel kinds of image manipulations, and has significant potential for other applications

Explainability: Relevance based Dynamic Deep Learning Algorithm for Fault Detection and Diagnosis in Chemical Processes

The focus of this work is on Statistical Process Control (SPC) of a manufacturing process based on available measurements. Two important applications of SPC in industrial settings are fault detection and diagnosis (FDD). In this work a deep learning (DL) based methodology is proposed for FDD. We investigate the application of an explainability concept to enhance the FDD accuracy of a deep neural network model trained with a data set of relatively small number of samples. The explainability is quantified by a novel relevance measure of input variables that is calculated from a Layerwise Relevance Propagation (LRP) algorithm. It is shown that the relevances can be used to discard redundant input feature vectors/ variables iteratively thus resulting in reduced over-fitting of noisy data, increasing distinguishability between output classes and superior FDD test accuracy. The efficacy of the proposed method is demonstrated on the benchmark Tennessee Eastman Process.Comment: Under Review. arXiv admin note: text overlap with arXiv:2012.0386

Towards Pixel-Level OOD Detection for Semantic Segmentation

There exists wide research surrounding the detection of out of distribution sample for image classification. Safety critical applications, such as autonomous driving, would benefit from the ability to localise the unusual objects causing an image to be out of distribution. This thesis adapts state-of-the-art methods for detecting out of distribution images for image classification to the new task of detecting out of distribution pixels, which can localise the unusual objects. It further experimentally compares the adapted methods to a new dataset derived from existing semantic segmentation datasets, proposing a new metric for the task. The evaluation shows that the performance ranking of the compared methods successfully transfers to the new task

Application of Deep Learning in Chemical Processes: Explainability, Monitoring and Observability

The last decade has seen remarkable advances in speech, image, and language recognition tools that have been made available to the public through computer and mobile devices’ applications. Most of these significant improvements were achieved by Artificial Intelligence (AI)/ deep learning (DL) algorithms (Hinton et al., 2006) that generally refers to a set of novel neural network architectures and algorithms such as long-short term memory (LSTM) units, convolutional networks (CNN), autoencoders (AE), t-distributed stochastic embedding (TSNE), etc. Although neural networks are not new, due to a combination of relatively novel improvements in methods for training the networks and the availability of increasingly powerful computers, one can now model much more complex nonlinear dynamic behaviour by using complex structures of neurons, i.e. more layers of neurons, than ever before (Goodfellow et al., 2016). However, it is recognized that the training of neural nets of such complex structures requires a vast amount of data. In this sense manufacturing processes are good candidates for deep learning applications since they utilize computers and information systems for monitoring and control thus generating a massive amount of data. This is especially true in pharmaceutical companies such as Sanofi Pasteur, the industrial collaborator for the current study, where large data sets are routinely stored for monitoring and regulatory purposes. Although novel DL algorithms have been applied with great success in image analysis, speech recognition, and language translation, their applications to chemical processes and pharmaceutical processes, in particular, are scarce. The current work deals with the investigation of deep learning in process systems engineering for three main areas of application: (i) Developing a deep learning classification model for profit-based operating regions. (ii) Developing both supervised and unsupervised process monitoring algorithms. (iii) Observability Analysis It is recognized that most empirical or black-box models, including DL models, have good generalization capabilities but are difficult to interpret. For example, using these methods it is difficult to understand how a particular decision is made, which input variable/feature is greatly influencing the decision made by the DL models etc. This understanding is expected to shed light on why biased results can be obtained or why a wrong class is predicted with a higher probability in classification problems. Hence, a key goal of the current work is on deriving process insights from DL models. To this end, the work proposes both supervised and unsupervised learning approaches to identify regions of process inputs that result in corresponding regions, i.e. ranges of values, of process profit. Furthermore, it will be shown that the ability to better interpret the model by identifying inputs that are most informative can be used to reduce over-fitting. To this end, a neural network (NN) pruning algorithm is developed that provides important physical insights on the system regarding the inputs that have positive and negative effect on profit function and to detect significant changes in process phenomenon. It is shown that pruning of input variables significantly reduces the number of parameters to be estimated and improves the classification test accuracy for both case studies: the Tennessee Eastman Process (TEP) and an industrial vaccine manufacturing process. The ability to store a large amount of data has permitted the use of deep learning (DL) and optimization algorithms for the process industries. In order to meet high levels of product quality, efficiency, and reliability, a process monitoring system is needed. The two aspects of Statistical Process Control (SPC) are fault detection and diagnosis (FDD). Many multivariate statistical methods like PCA and PLS and their dynamic variants have been extensively used for FD. However, the inherent non-linearities in the process pose challenges while using these linear models. Numerous deep learning FDD approaches have also been developed in the literature. However, the contribution plots for identifying the root cause of the fault have not been derived from Deep Neural Networks (DNNs). To this end, the supervised fault detection problem in the current work is formulated as a binary classification problem while the supervised fault diagnosis problem is formulated as a multi-class classification problem to identify the type of fault. Then, the application of the concept of explainability of DNNs is explored with its particular application in FDD problem. The developed methodology is demonstrated on TEP with non-incipient faults. Incipient faults are faulty conditions where signal to noise ratio is small and have not been widely studied in the literature. To address the same, a hierarchical dynamic deep learning algorithm is developed specifically to address the issue of fault detection and diagnosis of incipient faults. One of the major drawbacks of both the methods described above is the availability of labeled data i.e. normal operation and faulty operation data. From an industrial point of view, most data in an industrial setting, especially for biochemical processes, is obtained during normal operation and faulty data may not be available or may be insufficient. Hence, we also develop an unsupervised DL approach for process monitoring. It involves a novel objective function and a NN architecture that is tailored to detect the faults effectively. The idea is to learn the distribution of normal operation data to differentiate among the fault conditions. In order to demonstrate the advantages of the proposed methodology for fault detection, systematic comparisons are conducted with Multiway Principal Component Analysis (MPCA) and Multiway Partial Least Squares (MPLS) on an industrial scale Penicillin Simulator. Past investigations reported that the variability in productivity in the Sanofi's Pertussis Vaccine Manufacturing process may be highly correlated to biological phenomena, i.e. oxidative stresses, that are not routinely monitored by the company. While the company monitors and stores a large amount of fermentation data it may not be sufficiently informative about the underlying phenomena affecting the level of productivity. Furthermore, since the addition of new sensors in pharmaceutical processes requires extensive and expensive validation and certification procedures, it is very important to assess the potential ability of a sensor to observe relevant phenomena before its actual adoption in the manufacturing environment. This motivates the study of the observability of the phenomena from available data. An algorithm is proposed to check the observability for the classification task from the observed data (measurements). The proposed methodology makes use of a Supervised AE to reduce the dimensionality of the inputs. Thereafter, a criterion on the distance between the samples is used to calculate the percentage of overlap between the defined classes. The proposed algorithm is tested on the benchmark Tennessee Eastman process and then applied to the industrial vaccine manufacturing process