Subspace Representations and Learning for Visual Recognition

Pervasive and affordable sensor and storage technology enables the acquisition of an ever-rising amount of visual data. The ability to extract semantic information by interpreting, indexing and searching visual data is impacting domains such as surveillance, robotics, intelligence, human- computer interaction, navigation, healthcare, and several others. This further stimulates the investigation of automated extraction techniques that are more efficient, and robust against the many sources of noise affecting the already complex visual data, which is carrying the semantic information of interest. We address the problem by designing novel visual data representations, based on learning data subspace decompositions that are invariant against noise, while being informative for the task at hand. We use this guiding principle to tackle several visual recognition problems, including detection and recognition of human interactions from surveillance video, face recognition in unconstrained environments, and domain generalization for object recognition.;By interpreting visual data with a simple additive noise model, we consider the subspaces spanned by the model portion (model subspace) and the noise portion (variation subspace). We observe that decomposing the variation subspace against the model subspace gives rise to the so-called parity subspace. Decomposing the model subspace against the variation subspace instead gives rise to what we name invariant subspace. We extend the use of kernel techniques for the parity subspace. This enables modeling the highly non-linear temporal trajectories describing human behavior, and performing detection and recognition of human interactions. In addition, we introduce supervised low-rank matrix decomposition techniques for learning the invariant subspace for two other tasks. We learn invariant representations for face recognition from grossly corrupted images, and we learn object recognition classifiers that are invariant to the so-called domain bias.;Extensive experiments using the benchmark datasets publicly available for each of the three tasks, show that learning representations based on subspace decompositions invariant to the sources of noise lead to results comparable or better than the state-of-the-art

Domain Adaptation and Domain Generalization with Representation Learning

Machine learning has achieved great successes in the area of computer vision, especially in object recognition or classification. One of the core factors of the successes is the availability of massive labeled image or video data for training, collected manually by human. Labeling source training data, however, can be expensive and time consuming. Furthermore, a large amount of labeled source data may not always guarantee traditional machine learning techniques to generalize well; there is a potential bias or mismatch in the data, i.e., the training data do not represent the target environment. To mitigate the above dataset bias/mismatch, one can consider domain adaptation: utilizing labeled training data and unlabeled target data to develop a well-performing classifier on the target environment. In some cases, however, the unlabeled target data are nonexistent, but multiple labeled sources of data exist. Such situations can be addressed by domain generalization: using multiple source training sets to produce a classifier that generalizes on the unseen target domain. Although several domain adaptation and generalization approaches have been proposed, the domain mismatch in object recognition remains a challenging, open problem – the model performance has yet reached to a satisfactory level in real world applications. The overall goal of this thesis is to progress towards solving dataset bias in visual object recognition through representation learning in the context of domain adaptation and domain generalization. Representation learning is concerned with finding proper data representations or features via learning rather than via engineering by human experts. This thesis proposes several representation learning solutions based on deep learning and kernel methods. This thesis introduces a robust-to-noise deep neural network for handwritten digit classification trained on “clean” images only, which we name Deep Hybrid Network (DHN). DHNs are based on a particular combination of sparse autoencoders and restricted Boltzmann machines. The results show that DHN performs better than the standard deep neural network in recognizing digits with Gaussian and impulse noise, block and border occlusions. This thesis proposes the Domain Adaptive Neural Network (DaNN), a neural network based domain adaptation algorithm that minimizes the classification error and the domain discrepancy between the source and target data representations. The experiments show the competitiveness of DaNN against several state-of-the-art methods on a benchmark object dataset. This thesis develops the Multi-task Autoencoder (MTAE), a domain generalization algorithm based on autoencoders trained via multi-task learning. MTAE learns to transform the original image into its analogs in multiple related domains simultaneously. The results show that the MTAE’s representations provide better classification performance than some alternative autoencoder-based models as well as the current state-of-the-art domain generalization algorithms. This thesis proposes a fast kernel-based representation learning algorithm for both domain adaptation and domain generalization, Scatter Component Analysis (SCA). SCA finds a data representation that trades between maximizing the separability of classes, minimizing the mismatch between domains, and maximizing the separability of the whole data points. The results show that SCA performs much faster than some competitive algorithms, while providing state-of-the-art accuracy in both domain adaptation and domain generalization. Finally, this thesis presents the Deep Reconstruction-Classification Network (DRCN), a deep convolutional network for domain adaptation. DRCN learns to classify labeled source data and also to reconstruct unlabeled target data via a shared encoding representation. The results show that DRCN provides competitive or better performance than the prior state-of-the-art model on several cross-domain object datasets

Robust Machine Learning by Integrating Context

Intelligent software has the potential to transform our society. It is becoming the building block for many systems in the real world. However, despite the excellent performance of machine learning models on benchmarks, state-of-the-art methods like neural networks often fail once they encounter realistic settings. Since neural networks often learn correlations without reasoning with the right signals and knowledge, they fail when facing shifting distributions, unforeseen corruptions, and worst-case scenarios. Since neural networks are black-box models, they are not interpretable or trusted by the user. We need to build robust models for machine learning to be confidently and responsibly deployed in the most critical applications and systems. In this dissertation, I introduce our robust machine learning systems advancements by tightly integrating context into algorithms. The context has two aspects: the intrinsic structure of natural data, and the extrinsic structure from domain knowledge. Both are crucial: By capitalizing on the intrinsic structure in natural data, my work has shown that we can create robust machine learning systems, even in the worst case, an analytical result that also enjoys strong empirical gains. Through integrating external knowledge, such as the association between tasks and causal structure, my framework can instruct models to use the right signals for inference, enabling new opportunities for controllable and interpretable models. This thesis consists of three parts. In the first part, I aim to cover three works that use the intrinsic structure as a constraint to achieve robust inference. I present our framework that performs test-time optimization to respect the natural constraint, which is captured by self-supervised tasks. I illustrate that test-time optimization improves out-of-distribution generalization and adversarial robustness. Besides the inference algorithm, I show that intrinsic structure through discrete representations also improves out-of-distribution robustness. In the second part of the thesis, I then detail my work using external domain knowledge. I first introduce using causal structure from external domain knowledge to improve domain generalization robustness. I then show how the association of multiple tasks and regularization objectives helps robustness. In the final part of this dissertation, I show three works on trustworthy and reliable foundation models, a general-purpose model that will be the foundation for many AI applications. I show a framework that uses context to secure, interpret, and control foundation models

McNair Scholars Research Journal Volume IV

https://commons.stmarytx.edu/msrj/1003/thumbnail.jp

McNair Scholars Research Journal Volume IV

https://commons.stmarytx.edu/msrj/1003/thumbnail.jp

Through struggle and indifference: the UK academy's engagement with the open intellectual commons

The academy has long relied on publisher-facilitated research dissemination; yet digital dissemination has dramatically transformed the scholarly publishing field. Particularly, open access (OA) has disrupted an increasingly commodified and fetishised publishing praxis, creating an open intellectual commons. However, despite OA's public good, academics remain indifferent to its praxis. The UK academy's policy environment and cultural practices, represent a unique arena to consider these issues within. Limited research concerning the UK academy's rationales for OA engagement exists, particularly qualitative work critically evaluating influences and barriers to achieving cultural change. From a novel ethnographically-framed sociological perspective, combined with empirical investigations, this research addresses this gap in knowledge through comprehending academics' OA responses, publishing influences, actor power-relationships and related HE policy environments. A novel theoretical framework employing Marx, Foucault, Gramsci and the Italian Autonomous-Marxists' conceptualisations of power-relations, struggle and resistance, empower an ideological critique analysis. An examination of how increasingly marketised universities have embraced cognitive capitalism and academic alienation, contrasts with the tensions, events and concepts underlying UK OA's development. Extensive semi-structured interviews with different publishing actors provide cultural-native insights. OA practitioners expose the publication field's configuration, academics and other publishing actors' discourse develop further insights, while academic activists reveal how differing approaches affect dissemination praxis. Analysis indicates actors, including governmental bodies, commercial publishers and funders, dominate a hegemonic ruling-bloc, through controlling economic and symbolic esteem capital. An academy is revealed shifting from idealised OA, towards pragmatic compliance with a normative gold-OA form, although concerns about perceived cost barriers and diminished prestige capital remain. Despite ruling-bloc efforts to address the conjunctural crisis OA represents, a disaggregated counter-hegemonic resistance exists: providing platforms, sustainable publishing, and exposing inequities. While gold-OA praxis proliferates, a struggle for agency within scholarly publishing praxis continues. Hence, ostensibly future dissemination will contain OA elements, but its conformation remains uncertain