Learn, Generate, Rank, Explain: A Case Study of Visual Explanation by Generative Machine Learning

While the computer vision problem of searching for activities in videos is usually addressed by using discriminative models, their decisions tend to be opaque and difficult for people to understand. We propose a case study of a novel machine learning approach for generative searching and ranking of motion capture activities with visual explanation. Instead of directly ranking videos in the database given a text query, our approach uses a variant of Generative Adversarial Networks (GANs) to generate exemplars based on the query and uses them to search for the activity of interest in a large database. Our model is able to achieve comparable results to its discriminative counterpart, while being able to dynamically generate visual explanations. In addition to our searching and ranking method, we present an explanation interface that enables the user to successfully explore the model’s explanations and its confidence by revealing query-based, model-generated motion capture clips that contributed to the model’s decision. Finally, we conducted a user study with 44 participants to show that by using our model and interface, participants benefit from a deeper understanding of the model’s conceptualization of the search query. We discovered that the XAI system yielded a comparable level of efficiency, accuracy, and user-machine synchronization as its black-box counterpart, if the user exhibited a high level of trust for AI explanation.

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Counterfactual Examples for Data Augmentation: A Case Study

The International FLAIRS Conference Proceedings ◽

10.32473/flairs.v34i1.128503 ◽

2021 ◽

Vol 34 (1) ◽

Author(s):

Md Golam Moula Mehedi Hasan ◽

Douglas A. Talbert

Keyword(s):

Machine Learning ◽

Potential Application ◽

Data Augmentation ◽

Generative Adversarial Networks ◽

Application Area ◽

Learning Models ◽

Adversarial Networks ◽

Feature Values ◽

Machine Learning Models

Counterfactual explanations are gaining in popularity as a way of explaining machine learning models. Counterfactual examples are generally created to help interpret the decision of a model. In this case, if a model makes a certain decision for an instance, the counterfactual examples of that instance reverse the decision of the model. The counterfactual examples can be created by craftily changing particular feature values of the instance. Though counterfactual examples are generated to explain the decision of machine learning models, in this work, we explore another potential application area of counterfactual examples, whether counterfactual examples are useful for data augmentation. We demonstrate the efficacy of this approach on the widely used “Adult-Income” dataset. We consider several scenarios where we do not have enough data and use counterfactual examples to augment the dataset. We compare our approach with Generative Adversarial Networks approach for dataset augmentation. The experimental results show that our proposed approach can be an effective way to augment a dataset.

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Spectacular machinery and encrypted spectatorship

A Peer-Reviewed Journal About ◽

10.7146/aprja.v8i1.115423 ◽

2019 ◽

Vol 8 (1) ◽

pp. 170-182

Author(s):

Daniel Chávez Heras

Keyword(s):

Machine Learning ◽

Twentieth Century ◽

Popular Culture ◽

Generative Adversarial Networks ◽

Cultural Materialism ◽

Adversarial Networks ◽

Early Twentieth Century ◽

Production And Consumption ◽

Popular Images

This paper argues certain types of contemporary computation have a spectacular dimension which is consumed today as magic. Using popular images created through Generative Adversarial Networks (GANs) as a case study, I analyse the conditions of production and consumption of imagery generated through machine learning as a type of popular culture, I then compare this creative use of computing with magic shows and the cinema of attractions of the early twentieth century. This approach combines notions of digital cultural materialism with theories of early film spectatorship to suggest an emergent cultural trend: monstrative global computation as a form of spectacle.

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ORGANIC (1).pdf

10.26434/chemrxiv.5309668.v1 ◽

2017 ◽

Author(s):

Benjamin Sanchez-Lengeling ◽

Carlos Outeiral ◽

Gabriel L. Guimaraes ◽

Alan Aspuru-Guzik

Keyword(s):

Machine Learning ◽

Learning Community ◽

Chemical Species ◽

Material Design ◽

Organic Photovoltaic ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

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Paper2Wire – A Case Study of User-Centred Development of Machine Learning Tools for UX Designers

i-com ◽

10.1515/icom-2021-0002 ◽

2021 ◽

Vol 20 (1) ◽

pp. 19-32

Author(s):

Daniel Buschek ◽

Charlotte Anlauff ◽

Florian Lachner

Keyword(s):

Machine Learning ◽

Development Process ◽

User Study ◽

Concept Development ◽

Lessons Learned ◽

Design Tool ◽

Learning Tools ◽

Interface Elements ◽

Industry Partner

Abstract This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners’ needs and practices.

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Assessing the Relation between Mud Components and Rheology for Loss Circulation Prevention Using Polymeric Gels: A Machine Learning Approach

Energies ◽

10.3390/en14051377 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1377

Author(s):

Musaab I. Magzoub ◽

Raj Kiran ◽

Saeed Salehi ◽

Ibnelwaleed A. Hussein ◽

Mustafa S. Nasser

Keyword(s):

Machine Learning ◽

Rheological Properties ◽

Nearest Neighbor ◽

Drilling Fluid ◽

Gradient Boosting ◽

K Nearest Neighbor ◽

Wide Range ◽

Machine Learning Approach ◽

Drilling Operations

The traditional way to mitigate loss circulation in drilling operations is to use preventative and curative materials. However, it is difficult to quantify the amount of materials from every possible combination to produce customized rheological properties. In this study, machine learning (ML) is used to develop a framework to identify material composition for loss circulation applications based on the desired rheological characteristics. The relation between the rheological properties and the mud components for polyacrylamide/polyethyleneimine (PAM/PEI)-based mud is assessed experimentally. Four different ML algorithms were implemented to model the rheological data for various mud components at different concentrations and testing conditions. These four algorithms include (a) k-Nearest Neighbor, (b) Random Forest, (c) Gradient Boosting, and (d) AdaBoosting. The Gradient Boosting model showed the highest accuracy (91 and 74% for plastic and apparent viscosity, respectively), which can be further used for hydraulic calculations. Overall, the experimental study presented in this paper, together with the proposed ML-based framework, adds valuable information to the design of PAM/PEI-based mud. The ML models allowed a wide range of rheology assessments for various drilling fluid formulations with a mean accuracy of up to 91%. The case study has shown that with the appropriate combination of materials, reasonable rheological properties could be achieved to prevent loss circulation by managing the equivalent circulating density (ECD).

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On the Evaluation of Generative Adversarial Networks By Discriminative Models

2020 25th International Conference on Pattern Recognition (ICPR) ◽

10.1109/icpr48806.2021.9412214 ◽

2021 ◽

Author(s):

Amirsina Torfi ◽

Mohammadreza Beyki ◽

Edward A. Fox

Keyword(s):

Generative Adversarial Networks ◽

Discriminative Models ◽

Adversarial Networks

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Automatic Target Recognition for Low Resolution Foliage Penetrating SAR Images Using CNNs and GANs

Remote Sensing ◽

10.3390/rs13040596 ◽

2021 ◽

Vol 13 (4) ◽

pp. 596

Author(s):

David Vint ◽

Matthew Anderson ◽

Yuhao Yang ◽

Christos Ilioudis ◽

Gaetano Di Caterina ◽

...

Keyword(s):

Target Recognition ◽

Automatic Target Recognition ◽

Generative Adversarial Networks ◽

Low Resolution ◽

Sar Images ◽

Adversarial Networks ◽

Technological Advances ◽

Dataset Size ◽

Resolution Imaging ◽

High Level

In recent years, the technological advances leading to the production of high-resolution Synthetic Aperture Radar (SAR) images has enabled more and more effective target recognition capabilities. However, high spatial resolution is not always achievable, and, for some particular sensing modes, such as Foliage Penetrating Radars, low resolution imaging is often the only option. In this paper, the problem of automatic target recognition in Low Resolution Foliage Penetrating (FOPEN) SAR is addressed through the use of Convolutional Neural Networks (CNNs) able to extract both low and high level features of the imaged targets. Additionally, to address the issue of limited dataset size, Generative Adversarial Networks are used to enlarge the training set. Finally, a Receiver Operating Characteristic (ROC)-based post-classification decision approach is used to reduce classification errors and measure the capability of the classifier to provide a reliable output. The effectiveness of the proposed framework is demonstrated through the use of real SAR FOPEN data.

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