Gian Antonio Susto

University of Padua | UNIPD · Department of Information Engineering

Behavior-based Driver Identification is an emerging technology that recognizes drivers based on their unique driving behaviors, offering important applications such as vehicle theft prevention and personalized driving experiences. However, most studies fail to account for the real-world challenges of deploying Deep Learning models within vehicles....

Deep Reinforcement Learning is gaining increasing attention thanks to its capability to learn complex policies in high-dimensional settings. Recent advancements utilize a dual-network architecture to learn optimal policies through the Q-learning algorithm. However, this approach has notable drawbacks, such as an overestimation bias that can disrupt...

Visual Anomaly Detection (VAD) has gained significant research attention for its ability to identify anomalous images and pinpoint the specific areas responsible for the anomaly. A key advantage of VAD is its unsupervised nature, which eliminates the need for costly and time-consuming labeled data collection. However, despite its potential for real...

Continuous control Deep Reinforcement Learning (RL) approaches are known to suffer from estimation biases, leading to suboptimal policies. This paper introduces innovative methods in RL, focus-ing on addressing and exploiting estimation biases in Actor-Critic methods for continuous control tasks, using Deep Double Q-Learning. We design a Bias Explo...

Detecting objects in mobile robotics is crucial for numerous applications, from autonomous navigation to inspection. However, robots are often required to perform tasks in different domains with respect to the training one and need to adapt to these changes. Tiny mobile robots, subject to size, power, and computational constraints, encounter even m...

Object Detection is a highly relevant computer vision problem with many applications such as robotics and autonomous driving. Continual Learning~(CL) considers a setting where a model incrementally learns new information while retaining previously acquired knowledge. This is particularly challenging since Deep Learning models tend to catastrophical...

While numerous methods achieving remarkable performance exist in the Object Detection literature, addressing data distribution shifts remains challenging. Continual Learning (CL) offers solutions to this issue, enabling models to adapt to new data while maintaining performance on previous data. This is particularly pertinent for edge devices, commo...

In the following report, we describe the solution we propose for the AI Olympics competition held at IROS 2024. Our solution is based on a Model-free Deep Reinforcement Learning approach combined with an evolutionary strategy. We will briefly describe the algorithms that have been used and then provide details of the approach

The rapid growth of the global population, economy, and urbanization is significantly increasing energy consumption, necessitating the integration of renewable energy sources. This integration presents challenges that demand innovative solutions to maintain grid stability and efficiency. Smart grids offer enhanced reliability, efficiency, sustainab...

Climbing plants require an external support to grow vertically and enhance light acquisition. Climbers that find a suitable support demonstrate greater performance and fitness than those that remain prostrate. Support search is characterized by oscillatory movements (i.e., circumnutation), in which plants rotate around a central axis during their g...

Climbing plants require an external support to grow vertically and enhance light acquisition. Climbers that find a suitable support have greater performance and fitness than those that remain prostrate. Support search is characterized by oscillatory movements (i.e., circumnutation), in which plants rotate around a central axis during their growth....

Process monitoring and control are essential in modern industries for ensuring high quality standards and optimizing production performance. These technologies have a long history of application in production and have had numerous positive impacts, but also hold great potential when integrated with Industry 4.0 and advanced machine learning, partic...

An Anomaly Detection (AD) System for Self-diagnosis has been developed for Multiphase Flow Meter (MPFM). The system relies on machine learning algorithms for time series forecasting, historical data have been used to train a model and to predict the behavior of a sensor and, thus, to detect anomalies.

Anomaly Detection is a relevant problem that arises in numerous real-world applications, especially when dealing with images. However, there has been little research for this task in the Continual Learning setting. In this work, we introduce a novel approach called SCALE (SCALing is Enough) to perform Compressed Replay in a framework for Anomaly De...

The monitoring of rotating machinery has now become a fundamental activity in the industry, given the high criticality in production processes. Extracting useful information from relevant signals is a key factor for effective monitoring: studies in the areas of Informative Frequency Band selection (IFB) and Feature Extraction/Selection have demonst...

In the context of human-in-the-loop Machine Learning applications, like Decision Support Systems, interpretability approaches should provide actionable insights without making the users wait. In this paper, we propose Accelerated Model-agnostic Explanations (AcME), an interpretability approach that quickly provides feature importance scores both at...

Data-driven algorithms are studied and deployed in diverse domains to support critical decisions, directly impacting people’s well-being. As a result, a growing community of researchers has been investigating the equity of existing algorithms and proposing novel ones, advancing the understanding of risks and opportunities of automated decision-maki...

Continual Learning aims to learn from a stream of tasks, being able to remember at the same time both new and old tasks. While many approaches were proposed for single-class classification, multi-label classification in the continual scenario remains a challenging problem. For the first time, we study multi-label classification in the Domain Increm...

The detection of anomalous behaviours is an emerging need in many applications, particularly in contexts where security and reliability are critical aspects. While the definition of anomaly strictly depends on the domain framework, it is often impractical or too time consuming to obtain a fully labelled dataset. The use of unsupervised models to ov...

Unsupervised anomaly detection tackles the problem of finding anomalies inside datasets without the labels availability; since data tagging is typically hard or expensive to obtain, such approaches have seen huge applicability in recent years. In this context, Isolation Forest is a popular algorithm able to define an anomaly score by means of an en...

Data-driven Fault Detection and Classification approaches are becoming increasingly important in semiconductor manufacturing and in other industries aiming at implementing the Zero-defect paradigm. Two of the main challenges in developing such solutions are: (i) the complexity of sensor data, that typically presents themselves in the form of time-s...

The constantly increasing availability of data, the rapid expansion in computational and storage capacities of information technology systems, and algorithmic advances in Machine Learning (ML) and Artificial Intelligence (AI) are making a huge impact in the manufacturing industry for improving efficiency, operations and throughput. The semiconducto...

Adversarial Training has proved to be an effective training paradigm to enforce robustness against adversarial examples in modern neural network architectures. Despite many efforts, explanations of the foundational principles underpinning the effectiveness of Adversarial Training are limited and far from being widely accepted by the Deep Learning c...

Deception, also known as faking, is a critical issue when collecting data using questionnaires. As shown by previous studies, people have the tendency to fake their answers whenever they gain an advantage from doing so, e.g., when taking a test for a job application. Current methods identify the general attitude of faking but fail to identify fakin...

Learning useful representations of complex data has been the subject of extensive research for many years. In particular, with the diffusion of complex Deep Learning-based approaches in engineering applications, the possibility to interpret, to a certain degree, model predictions is of fundamental importance for both the model users and developers....

LEarning TO Rank (LETOR) algorithms are usually trained on annotated corpora where a single relevance label is assigned to each available document‐topic pair. Within the Cranfield framework, relevance labels result from merging either multiple expertly curated or crowdsourced human assessments. In this paper, we explore how to train LETOR models wi...

Learning to Rank (LETOR) algorithms are usually trained on annotated corpora where a single relevance label is assigned to each available document-topic pair. Within the Cranfield framework, relevance labels result from merging either multiple expertly curated or crowdsourced human assessments. In this paper, we explore how to train LETOR models wi...

Data-driven algorithms are being studied and deployed in diverse domains to support critical decisions, directly impacting on people's well-being. As a result, a growing community of algorithmic fairness researchers has been investigating the equity of existing algorithms and proposing novel ones, advancing the understanding of the risks and opport...

In manufacturing industries, it is of fundamental importance to detect anomalies in production in order to meet the required quality goals and to limit the number of defective products that are accidentally delivered to the customers. Nevertheless, monitoring systems currently employed in production are typically very simple and rely on a set of un...

The monitoring of rotating machinery is an essential task in today’s production processes. Currently, several machine learning and deep learning-based modules have achieved excellent results in fault detection and diagnosis. Nevertheless, to further increase user adoption and diffusion of such technologies, users and human experts must be provided...

Data-driven Anomaly Detection approaches have received increasing attention in many application areas in the past few years as a tool to monitor complex systems in addition to classical univariate control charts. Tree-based approaches have proven to be particularly effective when dealing with high-dimensional Anomaly Detection problems and with und...

In the context of human-in-the-loop Machine Learning applications, like Decision Support Systems, interpretability approaches should provide actionable insights without making the users wait. In this paper, we propose Accelerated Model-agnostic Explanations (AcME), an interpretability approach that quickly provides feature importance scores both at...

Adversarial robustness is one of the most challenging problems in Deep Learning and Computer Vision research. All the state-of-the-art techniques require a time-consuming procedure that creates cleverly perturbed images. Due to its cost, many solutions have been proposed to avoid Adversarial Training. However, all these attempts proved ineffective...

Unsupervised anomaly detection tackles the problem of finding anomalies inside datasets without the labels availability; since data tagging is typically hard or expensive to obtain, such approaches have seen huge applicability in recent years. In this context, Isolation Forest is a popular algorithm able to define an anomaly score by means of an en...

Predictive Maintenance technologies are particularly appealing for Industrial Equipment producers, as they pave the way to the selling of high added-value services and customized maintenance plans. However, standard Predictive Maintenance approaches assume the availability of sensor measurements, and the costs associated with adding sensors or remo...

The monitoring of rotating machinery is an essential activity for asset management today. Due to the large amount of monitored equipment, analyzing all the collected signals/features becomes an arduous task, leading the specialist to rely often on general alarms, which in turn can compromise the accuracy of the diagnosis. In order to make monitorin...

Machine Learning (ML) based technologies, like Virtual Metrology (VM)/Soft Sensing, Predictive Maintenance and Fault Detection, have been successfully applied in the past recent years in data intensive manufacturing industries, like semiconductor manufacturing, to improve process monitoring and related operations. Standardization and alignment over...

Ranking is a fundamental operation in information access systems, to filter information and direct user attention towards items deemed most relevant to them. Due to position bias, items of similar relevance may receive significantly different exposure, raising fairness concerns for item providers and motivating recent research into fair ranking. Wh...

Several faults affect heating, ventilation, and air conditioning (HVAC) chiller systems, leading to energy wastage, discomfort for the users, shorter equipment life, and system unreliability. Early detection of anomalies can prevent further deterioration of the chiller and energy wastage. In this work, a data-driven approach is used in order to det...

In oil and gas production, it is essential to monitor some performance indicators that are related to the composition of the extracted mixture, such as the liquid and gas content of the flow. These indicators cannot be directly measured and must be inferred with other measurements by using soft sensor approaches that model the target quantity. For...

Soft Sensors are data-driven technologies that allow to have estimations of quantities that are impossible or costly to be measured. Unfortunately, the design of effective soft sensors is heavily impacted by time-consuming feature engineering steps that may lead to sub-optimal information, especially when dealing with time-series input data. While...

Deep Learning approaches have revolutionized in the past decade the field of Computer Vision and, as a consequence, they are having a major impact in Industry 4.0 applications like automatic defect classification. Nevertheless, additional data, beside the image/video itself, is typically never exploited in a defect classification module: this aspec...

Ranking is a fundamental operation in information access systems, to filter information and direct user attention towards items deemed most relevant to them. Due to position bias, items of similar relevance may receive significantly different exposure, raising fairness concerns for item providers and motivating recent research into fair ranking. Wh...

We conduct an audit of pricing algorithms employed by companies in the Italian car insurance industry, primarily by gathering quotes through a popular comparison website. While acknowledging the complexity of the industry, we find evidence of several problematic practices. We show that birthplace and gender have a direct and sizeable impact on the...