Ultra-high molecular weight polyethylene (UHMWPE) is an engineering polymer that is utilized in many applications. Due to the presence of a transient physical network of entanglement, the processing of UHMWPE through conventional melt-extrusion techniques is extremely challenging. Reducing entanglement may provide a way to produce UHMWPE with superior mechanical properties by increasing the possibility of chain extension and chain orientation. A novel solvent-free route has been shown to produce disentangled UHMWPE by controlling the polymer synthesis. The polymerization conditions play a pivotal role in determining the entanglement density of UHMWPE. Homogeneous and heterogenous synthesis routes for the production of disentangled UHMWPE are viable. This Master Thesis performed sensitivity analysis on the controlled synthesis of disentangled UHMWPE based on MAO-activated bis(phenoxy imine) titanium dichloride catalytic system. Both Homogenous and heterogeneous synthesis routes were investigated and compared in terms of polymerization activity as well as entanglement density utilizing thermal, rheological, and morphological analysis techniques. Under the studied polymerization conditions, the catalytic system is able to produce disentangled UHMWPE with a minimum weight-average molecular weight of 4 million grams per mole via homogeneous as well as heterogeneous synthesis routes.

This dissertation tackles the problem of entanglement in Generative Adversarial Networks (GANs). The key insight is that disentanglement in GANs can be improved by differentiating between the content, and the operations performed on that content. For example, the identity of a generated face can be thought of as the content, while the lighting conditions can be thought of as the operations. We examine disentanglement in several kinds of deep networks. We examine image-to-image translation GANs, unconditional GANs, and sketch extraction networks. The task in image-to-image translation GANs is to translate images from one domain to another. It is immediately clear that disentanglement is necessary in this case. The network must maintain the core contents of the image while changing the stylistic appearance to match the target domain. We propose latent filter scaling to achieve multimodality and disentanglement. Previous methods require complicated network architectures to enforce that disentanglement. Our approach, on the other hand, maintains the traditional GAN loss with a minor change in architecture. Unlike image-to-image GANs, unconditional GANs are generally entangled. Unconditional GANs offer one method of changing the generated output which is changing the input noise code. Therefore, it is very difficult to resample only some parts of the generated images. We propose structured noise injection to achieve disentanglement in unconditional GANs. We propose using two input codes: one to specify spatially-variable details, and one to specify spatially-invariable details. In addition to the ability to change content and style independently, it also allows users to change the content only at certain locations. Combining our previous findings, we improve the performance of sketch-to-image translation networks. A crucial problem is how to correct input sketches before feeding them to the generator. By extracting sketches in an unsupervised way only from the spatially-variable branch of the image, we are able to produce sketches that show the content in many different styles. Those sketches can serve as a dataset to train a sketch-to-image translation GAN.

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3D-aware image synthesis has attracted increasing interest as it models the 3D nature of our real world. However, performing realistic object-level editing of the generated images in the multi-object scenario still remains a challenge. Recently, a 2D GAN termed BlobGAN has demonstrated great multi-object editing capabilities on real-world indoor scene datasets. In this work, we propose BlobGAN-3D, which is a 3D-aware improvement of the original 2D BlobGAN. We enable explicit camera pose control while maintaining the disentanglement for individual objects in the scene by extending the 2D blobs into 3D blobs. We keep the object-level editing capabilities of BlobGAN and in addition allow flexible control over the 3D location of the objects in the scene. We test our method on real-world indoor datasets and show that our method can achieve comparable image quality compared to the 2D BlobGAN and other 3D-aware GAN baselines while being the first to enable camera pose control and object-level editing in the challenging multi-object real-world scenarios.

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Generative models are one of the most prominent components of unsupervised learning models that have a plethora of applications in various domains such as image-to-image translation, video prediction, and generating synthetic data where accessing real data is expensive, unethical, or compromising privacy. One of the main challenges in designing a generative model is creating a disentangled representation of generative factors which gives control over various characteristics of the generated data. Since the architecture of variational autoencoders is centered around latent variables and their objective function directly governs the generative factors, they are the perfect choice for creating a more disentangled representation. However, these architectures generate samples that are blurry and of lower quality compared to other state-of-the-art generative models such as generative adversarial networks. Thus, we attempt to increase the disentanglement of latent variables in variational autoencoders without compromising the generated image quality. In this thesis, a novel generative model based on capsule networks and a variational autoencoder is proposed. Motivated by the concept of capsule neural networks and their vectorized output, these structures are employed to create a disentangled representation of latent features in variational autoencoders. In particular, the proposed structure, called CapsuleVAE, utilizes a capsule encoder whose vector outputs can translate to latent variables in a meaningful way. It is shown that CapsuleVAE generates results that are sharper and more diverse based on FID score and a metric inspired by the inception score. Furthermore, two different methods for training CapsuleVAE are proposed, and the generated results are investigated. In the first method, an objective function with regularization is proposed, and the optimal regularization hyperparameter is derived. In the second method, called sequential optimization, a novel training technique for training CapsuleVAE is proposed and the results are compared to the first method. Moreover, a novel metric for measuring disentanglement in latent variables is introduced. Based on this metric, it is shown that the proposed CapsuleVAE creates more disentangled representations. In summary, our proposed generative model enhances the disentanglement of latent variables which contributes to the model's generalizing well to new tasks and more control over the generated data. Our model also increases the generated image quality which addresses a common disadvantage in variational autoencoders. / Master of Science / Generative models are algorithms that, given a large enough initial dataset, create data points (such as images) similar to the initial dataset from random input numbers. These algorithms have various applications in different fields, such as generating synthetic healthcare data, wireless systems data generation in extreme or rare conditions, generating high-resolution, colorful images from grey-scale photos or sketches, and in general, generating synthetic data for applications where obtaining real data is expensive, inaccessible, unethical, or compromising privacy. Some generative models create a representation for the data and divide it into several ``generative factors". Researchers have shown that a better data representation is one where the generative factors are ``disentangled", meaning that each generative factor is responsible for only one particular feature in the generated data. Unfortunately, creating a model with disentangled generative factors sacrifices the image quality. In this work, we design a generative model that enhances the disentanglement of generative factors without compromising the quality of the generated images. In order to design a generative model with more disentangled generative factors, we employ capsule networks in the architecture of the generative model. Capsule networks are algorithms that classify the inputted information into different categories. We show that by using capsule networks, our designed generative model achieves higher performance in the quality of the generated images and creates a more disentangled representation of generative factors.

Recognizing visual information is crucial for many real artificial-intelligence-based applications, ranging from domestic robots to autonomous vehicles. However, the success of deep learning methods on visual recognition tasks is highly dependent on access to large-scale labeled datasets, which are expensive and cumbersome to collect. Transfer learning provides a way to alleviate the burden of annotating data, which transfers the knowledge learned from a rich-labeled source domain to a scarce-labeled target domain. However, the performance of deep learning models degrades significantly when testing on novel domains due to the presence of domain shift. To tackle the domain shift, conventional domain adaptation methods diminish the domain shift between two domains with a distribution matching loss or adversarial loss. These models align the domain-specific feature distribution and the domain-invariant feature distribution simultaneously, which is sub-optimal towards solving deep domain adaptation tasks, given that deep neural networks are known to extract features in which multiple hidden factors are highly entangled. This thesis explores how to learn effective transferable features by disentangling the deep features. The following questions are studied: (1) how to disentangle the deep features into domain-invariant and domain-specific features? (2) how would feature disentanglement help to learn transferable features under a synthetic-to-real domain adaptation scenario? (3) how would feature disentanglement facilitate transfer learning with multiple source or target domains? (4) how to leverage feature disentanglement to boost the performance in a federated system? To address these needs, this thesis proposes deep adversarial feature disentanglement: a class/domain identifier is trained on the labeled source domain and the disentangler generates features to fool the class/domain identifier. Extensive experiments and empirical analysis demonstrate the effectiveness of the feature disentanglement method on many real-world domain adaptation tasks. Specifically, the following three unsupervised domain adaptation scenarios are explored: (1) domain agnostic learning with disentangled representations, (2) unsupervised federated domain adaptation, (3) multi-source domain adaptation.

abstract: Disentangling latent spaces is an important research direction in the interpretability of unsupervised machine learning. Several recent works using deep learning are very effective at producing disentangled representations. However, in the unsupervised setting, there is no way to pre-specify which part of the latent space captures specific factors of variations. While this is generally a hard problem because of the non-existence of analytical expressions to capture these variations, there are certain factors like geometric transforms that can be expressed analytically. Furthermore, in existing frameworks, the disentangled values are also not interpretable. The focus of this work is to disentangle these geometric factors of variations (which turn out to be nuisance factors for many applications) from the semantic content of the signal in an interpretable manner which in turn makes the features more discriminative. Experiments are designed to show the modularity of the approach with other disentangling strategies as well as on multiple one-dimensional (1D) and two-dimensional (2D) datasets, clearly indicating the efficacy of the proposed approach. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2019

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Variational autoencoders (VAEs) are a neural network architecture broadly used in image generation (Doersch 2016). VAEs are neural network models that encode data from some domain and project it into a latent space (Doersch 2016). In doing so, the resulting encoding space goes from being a discrete distribution of vectors to a series of continuous manifolds. The latent space is subject to a Gaussian prior, giving the space some convenient properties for the distribution of said manifolds. Several strategies have been presented to try to disentangle said latent space to force each of their dimensions to have an interpretable meaning, for example, 𝛽-VAE, Factor-VAE, 𝛽-TCVAE. In this thesis, some previous VAE models for NaturalLanguage Processing (like Park and Lee (2021), and Bowman et al. (2015), where they finetune pretrained transformer models so they behave as VAEs, and where they used recurrent neural network language model to create a VAEs model that generates sentences in the continuous latent space, respectively) are combined with these disentangling techniques, to show if we can find any understandable meaning in the associated dimensions. The obtained results indicate that the techniques cannot be applied to text-based data without causing the model to suffer from posterior collapse.