Causality-Inspired Taxonomy for Explainable Artificial Intelligence

As two sides of the same coin, causality and explainable artificial intelligence (xAI) were initially proposed and developed with different goals. However, the latter can only be complete when seen through the lens of the causality framework. As such, we propose a novel causality-inspired framework for xAI that creates an environment for the development of xAI approaches. To show its applicability, biometrics was used as case study. For this, we have analysed 81 research papers on a myriad of biometric modalities and different tasks. We have categorised each of these methods according to our novel xAI Ladder and discussed the future directions of the field

An overview on user profiling in online social networks

Advances in Online Social Networks is creating huge data day in and out providing lot of opportunities to its users to express their interest and opinion. Due to the popularity and exposure of social networks, many intruders are using this platform for illegal purposes. Identifying such users is challenging and requires digging huge knowledge out of the data being flown in the social media. This work gives an insight to profile users in online social networks. User Profiles are established based on the behavioral patterns, correlations and activities of the user analyzed from the aggregated data using techniques like clustering, behavioral analysis, content analysis and face detection. Depending on application and purpose, the mechanism used in profiling users varies. Further study on other mechanisms used in profiling users is under the scope of future endeavors

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Discriminant feature pursuit: from statistical learning to informative learning.

Lin Dahua.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 233-250).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- The Problem We are Facing --- p.1Chapter 1.2 --- Generative vs. Discriminative Models --- p.2Chapter 1.3 --- Statistical Feature Extraction: Success and Challenge --- p.3Chapter 1.4 --- Overview of Our Works --- p.5Chapter 1.4.1 --- New Linear Discriminant Methods: Generalized LDA Formulation and Performance-Driven Sub space Learning --- p.5Chapter 1.4.2 --- Coupled Learning Models: Coupled Space Learning and Inter Modality Recognition --- p.6Chapter 1.4.3 --- Informative Learning Approaches: Conditional Infomax Learning and Information Chan- nel Model --- p.6Chapter 1.5 --- Organization of the Thesis --- p.8Chapter I --- History and Background --- p.10Chapter 2 --- Statistical Pattern Recognition --- p.11Chapter 2.1 --- Patterns and Classifiers --- p.11Chapter 2.2 --- Bayes Theory --- p.12Chapter 2.3 --- Statistical Modeling --- p.14Chapter 2.3.1 --- Maximum Likelihood Estimation --- p.14Chapter 2.3.2 --- Gaussian Model --- p.15Chapter 2.3.3 --- Expectation-Maximization --- p.17Chapter 2.3.4 --- Finite Mixture Model --- p.18Chapter 2.3.5 --- A Nonparametric Technique: Parzen Windows --- p.21Chapter 3 --- Statistical Learning Theory --- p.24Chapter 3.1 --- Formulation of Learning Model --- p.24Chapter 3.1.1 --- Learning: Functional Estimation Model --- p.24Chapter 3.1.2 --- Representative Learning Problems --- p.25Chapter 3.1.3 --- Empirical Risk Minimization --- p.26Chapter 3.2 --- Consistency and Convergence of Learning --- p.27Chapter 3.2.1 --- Concept of Consistency --- p.27Chapter 3.2.2 --- The Key Theorem of Learning Theory --- p.28Chapter 3.2.3 --- VC Entropy --- p.29Chapter 3.2.4 --- Bounds on Convergence --- p.30Chapter 3.2.5 --- VC Dimension --- p.35Chapter 4 --- History of Statistical Feature Extraction --- p.38Chapter 4.1 --- Linear Feature Extraction --- p.38Chapter 4.1.1 --- Principal Component Analysis (PCA) --- p.38Chapter 4.1.2 --- Linear Discriminant Analysis (LDA) --- p.41Chapter 4.1.3 --- Other Linear Feature Extraction Methods --- p.46Chapter 4.1.4 --- Comparison of Different Methods --- p.48Chapter 4.2 --- Enhanced Models --- p.49Chapter 4.2.1 --- Stochastic Discrimination and Random Subspace --- p.49Chapter 4.2.2 --- Hierarchical Feature Extraction --- p.51Chapter 4.2.3 --- Multilinear Analysis and Tensor-based Representation --- p.52Chapter 4.3 --- Nonlinear Feature Extraction --- p.54Chapter 4.3.1 --- Kernelization --- p.54Chapter 4.3.2 --- Dimension reduction by Manifold Embedding --- p.56Chapter 5 --- Related Works in Feature Extraction --- p.59Chapter 5.1 --- Dimension Reduction --- p.59Chapter 5.1.1 --- Feature Selection --- p.60Chapter 5.1.2 --- Feature Extraction --- p.60Chapter 5.2 --- Kernel Learning --- p.61Chapter 5.2.1 --- Basic Concepts of Kernel --- p.61Chapter 5.2.2 --- The Reproducing Kernel Map --- p.62Chapter 5.2.3 --- The Mercer Kernel Map --- p.64Chapter 5.2.4 --- The Empirical Kernel Map --- p.65Chapter 5.2.5 --- Kernel Trick and Kernelized Feature Extraction --- p.66Chapter 5.3 --- Subspace Analysis --- p.68Chapter 5.3.1 --- Basis and Subspace --- p.68Chapter 5.3.2 --- Orthogonal Projection --- p.69Chapter 5.3.3 --- Orthonormal Basis --- p.70Chapter 5.3.4 --- Subspace Decomposition --- p.70Chapter 5.4 --- Principal Component Analysis --- p.73Chapter 5.4.1 --- PCA Formulation --- p.73Chapter 5.4.2 --- Solution to PCA --- p.75Chapter 5.4.3 --- Energy Structure of PCA --- p.76Chapter 5.4.4 --- Probabilistic Principal Component Analysis --- p.78Chapter 5.4.5 --- Kernel Principal Component Analysis --- p.81Chapter 5.5 --- Independent Component Analysis --- p.83Chapter 5.5.1 --- ICA Formulation --- p.83Chapter 5.5.2 --- Measurement of Statistical Independence --- p.84Chapter 5.6 --- Linear Discriminant Analysis --- p.85Chapter 5.6.1 --- Fisher's Linear Discriminant Analysis --- p.85Chapter 5.6.2 --- Improved Algorithms for Small Sample Size Problem . --- p.89Chapter 5.6.3 --- Kernel Discriminant Analysis --- p.92Chapter II --- Improvement in Linear Discriminant Analysis --- p.100Chapter 6 --- Generalized LDA --- p.101Chapter 6.1 --- Regularized LDA --- p.101Chapter 6.1.1 --- Generalized LDA Implementation Procedure --- p.101Chapter 6.1.2 --- Optimal Nonsingular Approximation --- p.103Chapter 6.1.3 --- Regularized LDA algorithm --- p.104Chapter 6.2 --- A Statistical View: When is LDA optimal? --- p.105Chapter 6.2.1 --- Two-class Gaussian Case --- p.106Chapter 6.2.2 --- Multi-class Cases --- p.107Chapter 6.3 --- Generalized LDA Formulation --- p.108Chapter 6.3.1 --- Mathematical Preparation --- p.108Chapter 6.3.2 --- Generalized Formulation --- p.110Chapter 7 --- Dynamic Feedback Generalized LDA --- p.112Chapter 7.1 --- Basic Principle --- p.112Chapter 7.2 --- Dynamic Feedback Framework --- p.113Chapter 7.2.1 --- Initialization: K-Nearest Construction --- p.113Chapter 7.2.2 --- Dynamic Procedure --- p.115Chapter 7.3 --- Experiments --- p.115Chapter 7.3.1 --- Performance in Training Stage --- p.116Chapter 7.3.2 --- Performance on Testing set --- p.118Chapter 8 --- Performance-Driven Subspace Learning --- p.119Chapter 8.1 --- Motivation and Principle --- p.119Chapter 8.2 --- Performance-Based Criteria --- p.121Chapter 8.2.1 --- The Verification Problem and Generalized Average Margin --- p.122Chapter 8.2.2 --- Performance Driven Criteria based on Generalized Average Margin --- p.123Chapter 8.3 --- Optimal Subspace Pursuit --- p.125Chapter 8.3.1 --- Optimal threshold --- p.125Chapter 8.3.2 --- Optimal projection matrix --- p.125Chapter 8.3.3 --- Overall procedure --- p.129Chapter 8.3.4 --- Discussion of the Algorithm --- p.129Chapter 8.4 --- Optimal Classifier Fusion --- p.130Chapter 8.5 --- Experiments --- p.131Chapter 8.5.1 --- Performance Measurement --- p.131Chapter 8.5.2 --- Experiment Setting --- p.131Chapter 8.5.3 --- Experiment Results --- p.133Chapter 8.5.4 --- Discussion --- p.139Chapter III --- Coupled Learning of Feature Transforms --- p.140Chapter 9 --- Coupled Space Learning --- p.141Chapter 9.1 --- Introduction --- p.142Chapter 9.1.1 --- What is Image Style Transform --- p.142Chapter 9.1.2 --- Overview of our Framework --- p.143Chapter 9.2 --- Coupled Space Learning --- p.143Chapter 9.2.1 --- Framework of Coupled Modelling --- p.143Chapter 9.2.2 --- Correlative Component Analysis --- p.145Chapter 9.2.3 --- Coupled Bidirectional Transform --- p.148Chapter 9.2.4 --- Procedure of Coupled Space Learning --- p.151Chapter 9.3 --- Generalization to Mixture Model --- p.152Chapter 9.3.1 --- Coupled Gaussian Mixture Model --- p.152Chapter 9.3.2 --- Optimization by EM Algorithm --- p.152Chapter 9.4 --- Integrated Framework for Image Style Transform --- p.154Chapter 9.5 --- Experiments --- p.156Chapter 9.5.1 --- Face Super-resolution --- p.156Chapter 9.5.2 --- Portrait Style Transforms --- p.157Chapter 10 --- Inter-Modality Recognition --- p.162Chapter 10.1 --- Introduction to the Inter-Modality Recognition Problem . . . --- p.163Chapter 10.1.1 --- What is Inter-Modality Recognition --- p.163Chapter 10.1.2 --- Overview of Our Feature Extraction Framework . . . . --- p.163Chapter 10.2 --- Common Discriminant Feature Extraction --- p.165Chapter 10.2.1 --- Formulation of the Learning Problem --- p.165Chapter 10.2.2 --- Matrix-Form of the Objective --- p.168Chapter 10.2.3 --- Solving the Linear Transforms --- p.169Chapter 10.3 --- Kernelized Common Discriminant Feature Extraction --- p.170Chapter 10.4 --- Multi-Mode Framework --- p.172Chapter 10.4.1 --- Multi-Mode Formulation --- p.172Chapter 10.4.2 --- Optimization Scheme --- p.174Chapter 10.5 --- Experiments --- p.176Chapter 10.5.1 --- Experiment Settings --- p.176Chapter 10.5.2 --- Experiment Results --- p.177Chapter IV --- A New Perspective: Informative Learning --- p.180Chapter 11 --- Toward Information Theory --- p.181Chapter 11.1 --- Entropy and Mutual Information --- p.181Chapter 11.1.1 --- Entropy --- p.182Chapter 11.1.2 --- Relative Entropy (Kullback Leibler Divergence) --- p.184Chapter 11.2 --- Mutual Information --- p.184Chapter 11.2.1 --- Definition of Mutual Information --- p.184Chapter 11.2.2 --- Chain rules --- p.186Chapter 11.2.3 --- Information in Data Processing --- p.188Chapter 11.3 --- Differential Entropy --- p.189Chapter 11.3.1 --- Differential Entropy of Continuous Random Variable . --- p.189Chapter 11.3.2 --- Mutual Information of Continuous Random Variable . --- p.190Chapter 12 --- Conditional Infomax Learning --- p.191Chapter 12.1 --- An Overview --- p.192Chapter 12.2 --- Conditional Informative Feature Extraction --- p.193Chapter 12.2.1 --- Problem Formulation and Features --- p.193Chapter 12.2.2 --- The Information Maximization Principle --- p.194Chapter 12.2.3 --- The Information Decomposition and the Conditional Objective --- p.195Chapter 12.3 --- The Efficient Optimization --- p.197Chapter 12.3.1 --- Discrete Approximation Based on AEP --- p.197Chapter 12.3.2 --- Analysis of Terms and Their Derivatives --- p.198Chapter 12.3.3 --- Local Active Region Method --- p.200Chapter 12.4 --- Bayesian Feature Fusion with Sparse Prior --- p.201Chapter 12.5 --- The Integrated Framework for Feature Learning --- p.202Chapter 12.6 --- Experiments --- p.203Chapter 12.6.1 --- A Toy Problem --- p.203Chapter 12.6.2 --- Face Recognition --- p.204Chapter 13 --- Channel-based Maximum Effective Information --- p.209Chapter 13.1 --- Motivation and Overview --- p.209Chapter 13.2 --- Maximizing Effective Information --- p.211Chapter 13.2.1 --- Relation between Mutual Information and Classification --- p.211Chapter 13.2.2 --- Linear Projection and Metric --- p.212Chapter 13.2.3 --- Channel Model and Effective Information --- p.213Chapter 13.2.4 --- Parzen Window Approximation --- p.216Chapter 13.3 --- Parameter Optimization on Grassmann Manifold --- p.217Chapter 13.3.1 --- Grassmann Manifold --- p.217Chapter 13.3.2 --- Conjugate Gradient Optimization on Grassmann Manifold --- p.219Chapter 13.3.3 --- Computation of Gradient --- p.221Chapter 13.4 --- Experiments --- p.222Chapter 13.4.1 --- A Toy Problem --- p.222Chapter 13.4.2 --- Face Recognition --- p.223Chapter 14 --- Conclusion --- p.23

Bridging the gap between reconstruction and synthesis

Aplicat embargament des de la data de defensa fins el 15 de gener de 20223D reconstruction and image synthesis are two of the main pillars in computer vision. Early works focused on simple tasks such as multi-view reconstruction and texture synthesis. With the spur of Deep Learning, the field has rapidly progressed, making it possible to achieve more complex and high level tasks. For example, the 3D reconstruction results of traditional multi-view approaches are currently obtained with single view methods. Similarly, early pattern based texture synthesis works have resulted in techniques that allow generating novel high-resolution images. In this thesis we have developed a hierarchy of tools that cover all these range of problems, lying at the intersection of computer vision, graphics and machine learning. We tackle the problem of 3D reconstruction and synthesis in the wild. Importantly, we advocate for a paradigm in which not everything should be learned. Instead of applying Deep Learning naively we propose novel representations, layers and architectures that directly embed prior 3D geometric knowledge for the task of 3D reconstruction and synthesis. We apply these techniques to problems including scene/person reconstruction and photo-realistic rendering. We first address methods to reconstruct a scene and the clothed people in it while estimating the camera position. Then, we tackle image and video synthesis for clothed people in the wild. Finally, we bridge the gap between reconstruction and synthesis under the umbrella of a unique novel formulation. Extensive experiments conducted along this thesis show that the proposed techniques improve the performance of Deep Learning models in terms of the quality of the reconstructed 3D shapes / synthesised images, while reducing the amount of supervision and training data required to train them. In summary, we provide a variety of low, mid and high level algorithms that can be used to incorporate prior knowledge into different stages of the Deep Learning pipeline and improve performance in tasks of 3D reconstruction and image synthesis.La reconstrucció 3D i la síntesi d'imatges són dos dels pilars fonamentals en visió per computador. Els estudis previs es centren en tasques senzilles com la reconstrucció amb informació multi-càmera i la síntesi de textures. Amb l'aparició del "Deep Learning", aquest camp ha progressat ràpidament, fent possible assolir tasques molt més complexes. Per exemple, per obtenir una reconstrucció 3D, tradicionalment s'utilitzaven mètodes multi-càmera, en canvi ara, es poden obtenir a partir d'una sola imatge. De la mateixa manera, els primers treballs de síntesi de textures basats en patrons han donat lloc a tècniques que permeten generar noves imatges completes en alta resolució. En aquesta tesi, hem desenvolupat una sèrie d'eines que cobreixen tot aquest ventall de problemes, situats en la intersecció entre la visió per computador, els gràfics i l'aprenentatge automàtic. Abordem el problema de la reconstrucció i la síntesi 3D en el món real. És important destacar que defensem un paradigma on no tot s'ha d'aprendre. Enlloc d'aplicar el "Deep Learning" de forma naïve, proposem representacions novedoses i arquitectures que incorporen directament els coneixements geomètrics ja existents per a aconseguir la reconstrucció 3D i la síntesi d'imatges. Nosaltres apliquem aquestes tècniques a problemes com ara la reconstrucció d'escenes/persones i a la renderització d'imatges fotorealistes. Primer abordem els mètodes per reconstruir una escena, les persones vestides que hi ha i la posició de la càmera. A continuació, abordem la síntesi d'imatges i vídeos de persones vestides en situacions quotidianes. I finalment, aconseguim, a través d'una nova formulació única, connectar la reconstrucció amb la síntesi. Els experiments realitzats al llarg d'aquesta tesi demostren que les tècniques proposades milloren el rendiment dels models de "Deepp Learning" pel que fa a la qualitat de les reconstruccions i les imatges sintetitzades alhora que redueixen la quantitat de dades necessàries per entrenar-los. En resum, proporcionem una varietat d'algoritmes de baix, mitjà i alt nivell que es poden utilitzar per incorporar els coneixements previs a les diferents etapes del "Deep Learning" i millorar el rendiment en tasques de reconstrucció 3D i síntesi d'imatges.Postprint (published version

Bridging the Global Divide in AI Regulation: A Proposal for a Contextual, Coherent, and Commensurable Framework

This paper examines the current landscape of AI regulations, highlighting the divergent approaches being taken, and proposes an alternative contextual, coherent, and commensurable (3C) framework. The EU, Canada, South Korea, and Brazil follow a horizontal or lateral approach that postulates the homogeneity of AI systems, seeks to identify common causes of harm, and demands uniform human interventions. In contrast, the U.K., Israel, Switzerland, Japan, and China have pursued a context-specific or modular approach, tailoring regulations to the specific use cases of AI systems. The U.S. is reevaluating its strategy, with growing support for controlling existential risks associated with AI. Addressing such fragmentation of AI regulations is crucial to ensure the interoperability of AI. The present degree of proportionality, granularity, and foreseeability of the EU AI Act is not sufficient to garner consensus. The context-specific approach holds greater promises but requires further development in terms of details, coherency, and commensurability. To strike a balance, this paper proposes a hybrid 3C framework. To ensure contextuality, the framework categorizes AI into distinct types based on their usage and interaction with humans: autonomous, allocative, punitive, cognitive, and generative AI. To ensure coherency, each category is assigned specific regulatory objectives: safety for autonomous AI; fairness and explainability for allocative AI; accuracy and explainability for punitive AI; accuracy, robustness, and privacy for cognitive AI; and the mitigation of infringement and misuse for generative AI. To ensure commensurability, the framework promotes the adoption of international industry standards that convert principles into quantifiable metrics. In doing so, the framework is expected to foster international collaboration and standardization without imposing excessive compliance costs