Latent space factorisation and manipulation via matrix subspace projection

We tackle the problem disentangling the latent space of an autoencoder in order to separate labelled attribute information from other characteristic information. This then allows us to change selected attributes while preserving other information. Our method, matrix subspace projection, is much simpler than previous approaches to latent space factorisation, for example not requiring multiple discriminators or a careful weighting among their loss functions. Furthermore our new model can be applied to autoencoders as a plugin, and works across diverse domains such as images or text. We demonstrate the utility of our method for attribute manipulation in autoencoders trained across varied domains, using both human evaluation and automated methods. The quality of generation of our new model (e.g. reconstruction, conditional generation) is highly competitive to a number of strong baselines

LinkGAN: Linking GAN Latents to Pixels for Controllable Image Synthesis

This work presents an easy-to-use regularizer for GAN training, which helps explicitly link some axes of the latent space to a set of pixels in the synthesized image. Establishing such a connection facilitates a more convenient local control of GAN generation, where users can alter the image content only within a spatial area simply by partially resampling the latent code. Experimental results confirm four appealing properties of our regularizer, which we call LinkGAN. (1) The latent-pixel linkage is applicable to either a fixed region (\textit{i.e.}, same for all instances) or a particular semantic category (i.e., varying across instances), like the sky. (2) Two or multiple regions can be independently linked to different latent axes, which further supports joint control. (3) Our regularizer can improve the spatial controllability of both 2D and 3D-aware GAN models, barely sacrificing the synthesis performance. (4) The models trained with our regularizer are compatible with GAN inversion techniques and maintain editability on real images

Saliency for Image Description and Retrieval

We live in a world where we are surrounded by ever increasing numbers of images. More often than not, these images have very little metadata by which they can be indexed and searched. In order to avoid information overload, techniques need to be developed to enable these image collections to be searched by their content. Much of the previous work on image retrieval has used global features such as colour and texture to describe the content of the image. However, these global features are insufficient to accurately describe the image content when different parts of the image have different characteristics. This thesis initially discusses how this problem can be circumvented by using salient interest regions to select the areas of the image that are most interesting and generate local descriptors to describe the image characteristics in that region. The thesis discusses a number of different saliency detectors that are suitable for robust retrieval purposes and performs a comparison between a number of these region detectors. The thesis then discusses how salient regions can be used for image retrieval using a number of techniques, but most importantly, two techniques inspired from the field of textual information retrieval. Using these robust retrieval techniques, a new paradigm in image retrieval is discussed, whereby the retrieval takes place on a mobile device using a query image captured by a built-in camera. This paradigm is demonstrated in the context of an art gallery, in which the device can be used to find more information about particular images. The final chapter of the thesis discusses some approaches to bridging the semantic gap in image retrieval. The chapter explores ways in which un-annotated image collections can be searched by keyword. Two techniques are discussed; the first explicitly attempts to automatically annotate the un-annotated images so that the automatically applied annotations can be used for searching. The second approach does not try to explicitly annotate images, but rather, through the use of linear algebra, it attempts to create a semantic space in which images and keywords are positioned such that images are close to the keywords that represent them within the space

Sparse multivariate models for pattern detection in high-dimensional biological data

Recent advances in technology have made it possible and affordable to collect biological data of unprecedented size and complexity. While analysing such data, traditional statistical methods and machine learning algorithms suffer from the curse of dimensionality. Parsimonious models, which may refer to parsimony in model structure and/or model parameters, have been shown to improve both biological interpretability of the model and the generalisability to new data. In this thesis we are concerned with model selection in both supervised and unsupervised learning tasks. For supervised learnings, we propose a new penalty called graphguided group lasso (GGGL) and employ this penalty in penalised linear regressions. GGGL is able to integrate prior structured information with data mining, where variables sharing similar biological functions are collected into groups and the pairwise relatedness between groups are organised into a network. Such prior information will guide the selection of variables that are predictive to a univariate response, so that the model selects variable groups that are close in the network and important variables within the selected groups. We then generalise the idea of incorporating network-structured prior knowledge to association studies consisting of multivariate predictors and multivariate responses and propose the network-driven sparse reduced-rank regression (NsRRR). In NsRRR, pairwise relatedness between predictors and between responses are represented by two networks, and the model identifies associations between a subnetwork of predictors and a subnetwork of responses such that both subnetworks tend to be connected. For unsupervised learning, we are concerned with a multi-view learning task in which we compare the variance of high-dimensional biological features collected from multiple sources which are referred as “views”. We propose the sparse multi-view matrix factorisation (sMVMF) which is parsimonious in both model structure and model parameters. sMVMF can identify latent factors that regulate variability shared across all views and the variability which is characteristic to a specific view, respectively. For each novel method, we also present simulation studies and an application on real biological data to illustrate variable selection and model interpretability perspectives.Open Acces

Learning Density Models via Structured Latent Variables

As one principal approach to machine learning and cognitive science, the probabilistic framework has been continuously developed both theoretically and practically. Learning a probabilistic model can be thought of as inferring plausible models to explain observed data. The learning process exploits random variables as building blocks which are held together with probabilistic relationships. The key idea behind latent variable models is to introduce latent variables as powerful attributes (setting/instrument) to reveal data structures and explore underlying features which can sensitively describe the real-world data. The classical research approaches engage shallow architectures, including latent feature models and finite mixtures of latent variable models. Within the classical frameworks, we should make certain assumptions about the form, structure, and distribution of the data. Since the shallow form may not describe the data structures sufficiently, new types of latent structures are promptly developed with the probabilistic frameworks. In this line, three main research interests are sparked, including infinite latent feature models, mixtures of the mixture models, and deep models. This dissertation summarises our work which is advancing the state-of-the-art in both classical and emerging areas. In the first block, a finite latent variable model with the parametric priors is presented for clustering and is further extended into a two-layer mixture model for discrimination. These models embed the dimensionality reduction in their learning tasks by designing a latent structure called common loading. Referred to as the joint learning models, these models attain more appropriate low-dimensional space that better matches the learning task. Meanwhile, the parameters are optimised simultaneously for both the low-dimensional space and model learning. However, these joint learning models must assume the fixed number of features as well as mixtures, which are normally tuned and searched using a trial and error approach. In general, the simpler inference can be performed by fixing more parameters. However, the fixed parameters will limit the flexibility of models, and false assumptions could even derive incorrect inferences from the data. Thus, a richer model is allowed for reducing the number of assumptions. Therefore an infinite tri-factorisation structure is proposed with non-parametric priors in the second block. This model can automatically determine an optimal number of features and leverage the interrelation between data and features. In the final block, we introduce how to promote the shallow latent structures model to deep structures to handle the richer structured data. This part includes two tasks: one is a layer-wise-based model, another is a deep autoencoder-based model. In a deep density model, the knowledge of cognitive agents can be modelled using more complex probability distributions. At the same time, inference and parameter computation procedure are straightforward by using a greedy layer-wise algorithm. The deep autoencoder-based joint learning model is trained in an end-to-end fashion which does not require pre-training of the autoencoder network. Also, it can be optimised by standard backpropagation without the inference of maximum a posteriori. Deep generative models are much more efficient than their shallow architectures for unsupervised and supervised density learning tasks. Furthermore, they can also be developed and used in various practical applications

Algorithms, applications and systems towards interpretable pattern mining from multi-aspect data

How do humans move around in the urban space and how do they differ when the city undergoes terrorist attacks? How do users behave in Massive Open Online courses~(MOOCs) and how do they differ if some of them achieve certificates while some of them not? What areas in the court elite players, such as Stephen Curry, LeBron James, like to make their shots in the course of the game? How can we uncover the hidden habits that govern our online purchases? Are there unspoken agendas in how different states pass legislation of certain kinds? At the heart of these seemingly unconnected puzzles is this same mystery of multi-aspect mining, i.g., how can we mine and interpret the hidden pattern from a dataset that simultaneously reveals the associations, or changes of the associations, among various aspects of the data (e.g., a shot could be described with three aspects, player, time of the game, and area in the court)? Solving this problem could open gates to a deep understanding of underlying mechanisms for many real-world phenomena. While much of the research in multi-aspect mining contribute broad scope of innovations in the mining part, interpretation of patterns from the perspective of users (or domain experts) is often overlooked. Questions like what do they require for patterns, how good are the patterns, or how to read them, have barely been addressed. Without efficient and effective ways of involving users in the process of multi-aspect mining, the results are likely to lead to something difficult for them to comprehend. This dissertation proposes the M^3 framework, which consists of multiplex pattern discovery, multifaceted pattern evaluation, and multipurpose pattern presentation, to tackle the challenges of multi-aspect pattern discovery. Based on this framework, we develop algorithms, applications, and analytic systems to enable interpretable pattern discovery from multi-aspect data. Following the concept of meaningful multiplex pattern discovery, we propose PairFac to close the gap between human information needs and naive mining optimization. We demonstrate its effectiveness in the context of impact discovery in the aftermath of urban disasters. We develop iDisc to target the crossing of multiplex pattern discovery with multifaceted pattern evaluation. iDisc meets the specific information need in understanding multi-level, contrastive behavior patterns. As an example, we use iDisc to predict student performance outcomes in Massive Open Online Courses given users' latent behaviors. FacIt is an interactive visual analytic system that sits at the intersection of all three components and enables for interpretable, fine-tunable, and scrutinizable pattern discovery from multi-aspect data. We demonstrate each work's significance and implications in its respective problem context. As a whole, this series of studies is an effort to instantiate the M^3 framework and push the field of multi-aspect mining towards a more human-centric process in real-world applications

Dynamical structure in neural population activity

The question of how the collective activity of neural populations in the brain gives rise to complex behaviour is fundamental to neuroscience. At the core of this question lie considerations about how neural circuits can perform computations that enable sensory perception, motor control, and decision making. It is thought that such computations are implemented by the dynamical evolution of distributed activity in recurrent circuits. Thus, identifying and interpreting dynamical structure in neural population activity is a key challenge towards a better understanding of neural computation. In this thesis, I make several contributions in addressing this challenge. First, I develop two novel methods for neural data analysis. Both methods aim to extract trajectories of low-dimensional computational state variables directly from the unbinned spike-times of simultaneously recorded neurons on single trials. The first method separates inter-trial variability in the low-dimensional trajectory from variability in the timing of progression along its path, and thus offers a quantification of inter-trial variability in the underlying computational process. The second method simultaneously learns a low-dimensional portrait of the underlying nonlinear dynamics of the circuit, as well as the system's fixed points and locally linearised dynamics around them. This approach facilitates extracting interpretable low-dimensional hypotheses about computation directly from data. Second, I turn to the question of how low-dimensional dynamical structure may be embedded within a high-dimensional neurobiological circuit with excitatory and inhibitory cell-types. I analyse how such circuit-level features shape population activity, with particular focus on responses to targeted optogenetic perturbations of the circuit. Third, I consider the problem of implementing multiple computations in a single dynamical system. I address this in the framework of multi-task learning in recurrently connected networks and demonstrate that a careful organisation of low-dimensional, activity-defined subspaces within the network can help to avoid interference across tasks

Discovering motion hierarchies via tree-structured coding of trajectories

International audienceThe dynamic content of physical scenes is largely compositional, that is, the movements of the objects and of their parts are hierarchically organised and relate through composition along this hierarchy. This structure also prevails in the apparent 2D motion that a video captures. Accessing this visual motion hierarchy is important to get a better understanding of dynamic scenes and is useful for video manipulation. We propose to capture it through learned, tree-structured sparse coding of point trajectories. We leverage this new representation within an unsupervised clustering scheme to partition hierarchically the trajectories into meaningful groups. We show through experiments on motion capture data that our model is able to extract moving segments along with their organisation. We also present competitive results on the task of segmenting objects in video sequences from trajectories