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- All Subjects: Computer Science
- Genre: Academic theses
Description
Machine learning models convert raw data in the form of video, images, audio,
text, etc. into feature representations that are convenient for computational process-
ing. Deep neural networks have proven to be very efficient feature extractors for a
variety of machine learning tasks. Generative models based on deep neural networks
introduce constraints on the feature space to learn transferable and disentangled rep-
resentations. Transferable feature representations help in training machine learning
models that are robust across different distributions of data. For example, with the
application of transferable features in domain adaptation, models trained on a source
distribution can be applied to a data from a target distribution even though the dis-
tributions may be different. In style transfer and image-to-image translation, disen-
tangled representations allow for the separation of style and content when translating
images.
This thesis examines learning transferable data representations in novel deep gen-
erative models. The Semi-Supervised Adversarial Translator (SAT) utilizes adversar-
ial methods and cross-domain weight sharing in a neural network to extract trans-
ferable representations. These transferable interpretations can then be decoded into
the original image or a similar image in another domain. The Explicit Disentangling
Network (EDN) utilizes generative methods to disentangle images into their core at-
tributes and then segments sets of related attributes. The EDN can separate these
attributes by controlling the ow of information using a novel combination of losses
and network architecture. This separation of attributes allows precise modi_cations
to speci_c components of the data representation, boosting the performance of ma-
chine learning tasks. The effectiveness of these models is evaluated across domain
adaptation, style transfer, and image-to-image translation tasks.
text, etc. into feature representations that are convenient for computational process-
ing. Deep neural networks have proven to be very efficient feature extractors for a
variety of machine learning tasks. Generative models based on deep neural networks
introduce constraints on the feature space to learn transferable and disentangled rep-
resentations. Transferable feature representations help in training machine learning
models that are robust across different distributions of data. For example, with the
application of transferable features in domain adaptation, models trained on a source
distribution can be applied to a data from a target distribution even though the dis-
tributions may be different. In style transfer and image-to-image translation, disen-
tangled representations allow for the separation of style and content when translating
images.
This thesis examines learning transferable data representations in novel deep gen-
erative models. The Semi-Supervised Adversarial Translator (SAT) utilizes adversar-
ial methods and cross-domain weight sharing in a neural network to extract trans-
ferable representations. These transferable interpretations can then be decoded into
the original image or a similar image in another domain. The Explicit Disentangling
Network (EDN) utilizes generative methods to disentangle images into their core at-
tributes and then segments sets of related attributes. The EDN can separate these
attributes by controlling the ow of information using a novel combination of losses
and network architecture. This separation of attributes allows precise modi_cations
to speci_c components of the data representation, boosting the performance of ma-
chine learning tasks. The effectiveness of these models is evaluated across domain
adaptation, style transfer, and image-to-image translation tasks.
ContributorsEusebio, Jose Miguel Ang (Author) / Panchanathan, Sethuraman (Thesis advisor) / Davulcu, Hasan (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2018
Description
Facial Expressions Recognition using the Convolution Neural Network has been actively researched upon in the last decade due to its high number of applications in the human-computer interaction domain. As Convolution Neural Networks have the exceptional ability to learn, they outperform the methods using handcrafted features. Though the state-of-the-art models achieve high accuracy on the lab-controlled images, they still struggle for the wild expressions. Wild expressions are captured in a real-world setting and have natural expressions. Wild databases have many challenges such as occlusion, variations in lighting conditions and head poses. In this work, I address these challenges and propose a new model containing a Hybrid Convolutional Neural Network with a Fusion Layer. The Fusion Layer utilizes a combination of the knowledge obtained from two different domains for enhanced feature extraction from the in-the-wild images. I tested my network on two publicly available in-the-wild datasets namely RAF-DB and AffectNet. Next, I tested my trained model on CK+ dataset for the cross-database evaluation study. I prove that my model achieves comparable results with state-of-the-art methods. I argue that it can perform well on such datasets because it learns the features from two different domains rather than a single domain. Last, I present a real-time facial expression recognition system as a part of this work where the images are captured in real-time using laptop camera and passed to the model for obtaining a facial expression label for it. It indicates that the proposed model has low processing time and can produce output almost instantly.
ContributorsChhabra, Sachin (Author) / Li, Baoxin (Thesis advisor) / Venkateswara, Hemanth (Committee member) / Srivastava, Siddharth (Committee member) / Arizona State University (Publisher)
Created2019
Description
Fatigue in radiology is a readily studied area. Machine learning concepts appliedto the identification of fatigue are also readily available. However, the intersection
between the two areas is not a relative commonality. This study looks to explore the
intersection of fatigue in radiology and machine learning concepts by analyzing temporal
trends in multivariate time series data. A novel methodological approach using support
vector machines to observe temporal trends in time-based aggregations of time series data
is proposed. The data used in the study is captured in a real-world, unconstrained
radiology setting where gaze and facial metrics are captured from radiologists performing
live image reviews. The captured data is formatted into classes whose labels represent a
window of time during the radiologist’s review. Using the labeled classes, the decision
function and accuracy of trained, linear support vector machine models are evaluated to
produce a visualization of temporal trends and critical inflection points as well as the
contribution of individual features. Consequently, the study finds valid potential
justification in the methods suggested. The study offers a prospective use of maximummargin classification to demarcate the manipulation of an abstract phenomenon such as
fatigue on temporal data. Potential applications are envisioned that could improve the
workload distribution of the medical act.
ContributorsHayes, Matthew (Author) / McDaniel, Troy (Thesis advisor) / Coza, Aurel (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2022
Description
Endowing machines with the ability to understand digital images is a critical task for a host of high-impact applications, including pathology detection in radiographic imaging, autonomous vehicles, and assistive technology for the visually impaired. Computer vision systems rely on large corpora of annotated data in order to train task-specific visual recognition models. Despite significant advances made over the past decade, the fact remains collecting and annotating the data needed to successfully train a model is a prohibitively expensive endeavor. Moreover, these models are prone to rapid performance degradation when applied to data sampled from a different domain. Recent works in the development of deep adaptation networks seek to overcome these challenges by facilitating transfer learning between source and target domains. In parallel, the unification of dominant semi-supervised learning techniques has illustrated unprecedented potential for utilizing unlabeled data to train classification models in defiance of discouragingly meager sets of annotated data.
In this thesis, a novel domain adaptation algorithm -- Domain Adaptive Fusion (DAF) -- is proposed, which encourages a domain-invariant linear relationship between the pixel-space of different domains and the prediction-space while being trained under a domain adversarial signal. The thoughtful combination of key components in unsupervised domain adaptation and semi-supervised learning enable DAF to effectively bridge the gap between source and target domains. Experiments performed on computer vision benchmark datasets for domain adaptation endorse the efficacy of this hybrid approach, outperforming all of the baseline architectures on most of the transfer tasks.
In this thesis, a novel domain adaptation algorithm -- Domain Adaptive Fusion (DAF) -- is proposed, which encourages a domain-invariant linear relationship between the pixel-space of different domains and the prediction-space while being trained under a domain adversarial signal. The thoughtful combination of key components in unsupervised domain adaptation and semi-supervised learning enable DAF to effectively bridge the gap between source and target domains. Experiments performed on computer vision benchmark datasets for domain adaptation endorse the efficacy of this hybrid approach, outperforming all of the baseline architectures on most of the transfer tasks.
ContributorsDudley, Andrew, M.S (Author) / Panchanathan, Sethuraman (Thesis advisor) / Venkateswara, Hemanth (Committee member) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2019
Description
Parents fulfill a pivotal role in early childhood development of social and communication
skills. In children with autism, the development of these skills can be delayed. Applied
behavioral analysis (ABA) techniques have been created to aid in skill acquisition.
Among these, pivotal response treatment (PRT) has been empirically shown to foster
improvements. Research into PRT implementation has also shown that parents can be
trained to be effective interventionists for their children. The current difficulty in PRT
training is how to disseminate training to parents who need it, and how to support and
motivate practitioners after training.
Evaluation of the parents’ fidelity to implementation is often undertaken using video
probes that depict the dyadic interaction occurring between the parent and the child during
PRT sessions. These videos are time consuming for clinicians to process, and often result
in only minimal feedback for the parents. Current trends in technology could be utilized to
alleviate the manual cost of extracting data from the videos, affording greater
opportunities for providing clinician created feedback as well as automated assessments.
The naturalistic context of the video probes along with the dependence on ubiquitous
recording devices creates a difficult scenario for classification tasks. The domain of the
PRT video probes can be expected to have high levels of both aleatory and epistemic
uncertainty. Addressing these challenges requires examination of the multimodal data
along with implementation and evaluation of classification algorithms. This is explored
through the use of a new dataset of PRT videos.
The relationship between the parent and the clinician is important. The clinician can
provide support and help build self-efficacy in addition to providing knowledge and
modeling of treatment procedures. Facilitating this relationship along with automated
feedback not only provides the opportunity to present expert feedback to the parent, but
also allows the clinician to aid in personalizing the classification models. By utilizing a
human-in-the-loop framework, clinicians can aid in addressing the uncertainty in the
classification models by providing additional labeled samples. This will allow the system
to improve classification and provides a person-centered approach to extracting
multimodal data from PRT video probes.
skills. In children with autism, the development of these skills can be delayed. Applied
behavioral analysis (ABA) techniques have been created to aid in skill acquisition.
Among these, pivotal response treatment (PRT) has been empirically shown to foster
improvements. Research into PRT implementation has also shown that parents can be
trained to be effective interventionists for their children. The current difficulty in PRT
training is how to disseminate training to parents who need it, and how to support and
motivate practitioners after training.
Evaluation of the parents’ fidelity to implementation is often undertaken using video
probes that depict the dyadic interaction occurring between the parent and the child during
PRT sessions. These videos are time consuming for clinicians to process, and often result
in only minimal feedback for the parents. Current trends in technology could be utilized to
alleviate the manual cost of extracting data from the videos, affording greater
opportunities for providing clinician created feedback as well as automated assessments.
The naturalistic context of the video probes along with the dependence on ubiquitous
recording devices creates a difficult scenario for classification tasks. The domain of the
PRT video probes can be expected to have high levels of both aleatory and epistemic
uncertainty. Addressing these challenges requires examination of the multimodal data
along with implementation and evaluation of classification algorithms. This is explored
through the use of a new dataset of PRT videos.
The relationship between the parent and the clinician is important. The clinician can
provide support and help build self-efficacy in addition to providing knowledge and
modeling of treatment procedures. Facilitating this relationship along with automated
feedback not only provides the opportunity to present expert feedback to the parent, but
also allows the clinician to aid in personalizing the classification models. By utilizing a
human-in-the-loop framework, clinicians can aid in addressing the uncertainty in the
classification models by providing additional labeled samples. This will allow the system
to improve classification and provides a person-centered approach to extracting
multimodal data from PRT video probes.
ContributorsCopenhaver Heath, Corey D (Author) / Panchanathan, Sethuraman (Thesis advisor) / McDaniel, Troy (Committee member) / Venkateswara, Hemanth (Committee member) / Davulcu, Hasan (Committee member) / Gaffar, Ashraf (Committee member) / Arizona State University (Publisher)
Created2019
Description
Feature embeddings differ from raw features in the sense that the former obey certain properties like notion of similarity/dissimilarity in it's embedding space. word2vec is a preeminent example in this direction, where the similarity in the embedding space is measured in terms of the cosine similarity. Such language embedding models have seen numerous applications in both language and vision community as they capture the information in the modality (English language) efficiently. Inspired by these language models, this work focuses on learning embedding spaces for two visual computing tasks, 1. Image Hashing 2. Zero Shot Learning. The training set was used to learn embedding spaces over which similarity/dissimilarity is measured using several distance metrics like hamming / euclidean / cosine distances. While the above-mentioned language models learn generic word embeddings, in this work task specific embeddings were learnt which can be used for Image Retrieval and Classification separately.
Image Hashing is the task of mapping images to binary codes such that some notion of user-defined similarity is preserved. The first part of this work focuses on designing a new framework that uses the hash-tags associated with web images to learn the binary codes. Such codes can be used in several applications like Image Retrieval and Image Classification. Further, this framework requires no labelled data, leaving it very inexpensive. Results show that the proposed approach surpasses the state-of-art approaches by a significant margin.
Zero-shot classification is the task of classifying the test sample into a new class which was not seen during training. This is possible by establishing a relationship between the training and the testing classes using auxiliary information. In the second part of this thesis, a framework is designed that trains using the handcrafted attribute vectors and word vectors but doesn’t require the expensive attribute vectors during test time. More specifically, an intermediate space is learnt between the word vector space and the image feature space using the hand-crafted attribute vectors. Preliminary results on two zero-shot classification datasets show that this is a promising direction to explore.
Image Hashing is the task of mapping images to binary codes such that some notion of user-defined similarity is preserved. The first part of this work focuses on designing a new framework that uses the hash-tags associated with web images to learn the binary codes. Such codes can be used in several applications like Image Retrieval and Image Classification. Further, this framework requires no labelled data, leaving it very inexpensive. Results show that the proposed approach surpasses the state-of-art approaches by a significant margin.
Zero-shot classification is the task of classifying the test sample into a new class which was not seen during training. This is possible by establishing a relationship between the training and the testing classes using auxiliary information. In the second part of this thesis, a framework is designed that trains using the handcrafted attribute vectors and word vectors but doesn’t require the expensive attribute vectors during test time. More specifically, an intermediate space is learnt between the word vector space and the image feature space using the hand-crafted attribute vectors. Preliminary results on two zero-shot classification datasets show that this is a promising direction to explore.
ContributorsGattupalli, Jaya Vijetha (Author) / Li, Baoxin (Thesis advisor) / Yang, Yezhou (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2019
Description
Access to real-time situational information including the relative position and motion of surrounding objects is critical for safe and independent travel. Object or obstacle (OO) detection at a distance is primarily a task of the visual system due to the high resolution information the eyes are able to receive from afar. As a sensory organ in particular, the eyes have an unparalleled ability to adjust to varying degrees of light, color, and distance. Therefore, in the case of a non-visual traveler, someone who is blind or low vision, access to visual information is unattainable if it is positioned beyond the reach of the preferred mobility device or outside the path of travel. Although, the area of assistive technology in terms of electronic travel aids (ETA’s) has received considerable attention over the last two decades; surprisingly, the field has seen little work in the area focused on augmenting rather than replacing current non-visual travel techniques, methods, and tools. Consequently, this work describes the design of an intuitive tactile language and series of wearable tactile interfaces (the Haptic Chair, HaptWrap, and HapBack) to deliver real-time spatiotemporal data. The overall intuitiveness of the haptic mappings conveyed through the tactile interfaces are evaluated using a combination of absolute identification accuracy of a series of patterns and subjective feedback through post-experiment surveys. Two types of spatiotemporal representations are considered: static patterns representing object location at a single time instance, and dynamic patterns, added in the HaptWrap, which represent object movement over a time interval. Results support the viability of multi-dimensional haptics applied to the body to yield an intuitive understanding of dynamic interactions occurring around the navigator during travel. Lastly, it is important to point out that the guiding principle of this work centered on providing the navigator with spatial knowledge otherwise unattainable through current mobility techniques, methods, and tools, thus, providing the \emph{navigator} with the information necessary to make informed navigation decisions independently, at a distance.
ContributorsDuarte, Bryan Joiner (Author) / McDaniel, Troy (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2020
Description
In the last decade deep learning based models have revolutionized machine learning and computer vision applications. However, these models are data-hungry and training them is a time-consuming process. In addition, when deep neural networks are updated to augment their prediction space with new data, they run into the problem of catastrophic forgetting, where the model forgets previously learned knowledge as it overfits to the newly available data. Incremental learning algorithms enable deep neural networks to prevent catastrophic forgetting by retaining knowledge of previously observed data while also learning from newly available data.
This thesis presents three models for incremental learning; (i) Design of an algorithm for generative incremental learning using a pre-trained deep neural network classifier; (ii) Development of a hashing based clustering algorithm for efficient incremental learning; (iii) Design of a student-teacher coupled neural network to distill knowledge for incremental learning. The proposed algorithms were evaluated using popular vision datasets for classification tasks. The thesis concludes with a discussion about the feasibility of using these techniques to transfer information between networks and also for incremental learning applications.
This thesis presents three models for incremental learning; (i) Design of an algorithm for generative incremental learning using a pre-trained deep neural network classifier; (ii) Development of a hashing based clustering algorithm for efficient incremental learning; (iii) Design of a student-teacher coupled neural network to distill knowledge for incremental learning. The proposed algorithms were evaluated using popular vision datasets for classification tasks. The thesis concludes with a discussion about the feasibility of using these techniques to transfer information between networks and also for incremental learning applications.
ContributorsPatil, Rishabh (Author) / Venkateswara, Hemanth (Thesis advisor) / Panchanathan, Sethuraman (Thesis advisor) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2020
Description
Humans have a great ability to recognize objects in different environments irrespective of their variations. However, the same does not apply to machine learning models which are unable to generalize to images of objects from different domains. The generalization of these models to new data is constrained by the domain gap. Many factors such as image background, image resolution, color, camera perspective and variations in the objects are responsible for the domain gap between the training data (source domain) and testing data (target domain). Domain adaptation algorithms aim to overcome the domain gap between the source and target domains and learn robust models that can perform well across both the domains.
This thesis provides solutions for the standard problem of unsupervised domain adaptation (UDA) and the more generic problem of generalized domain adaptation (GDA). The contributions of this thesis are as follows. (1) Certain and Consistent Domain Adaptation model for closed-set unsupervised domain adaptation by aligning the features of the source and target domain using deep neural networks. (2) A multi-adversarial deep learning model for generalized domain adaptation. (3) A gating model that detects out-of-distribution samples for generalized domain adaptation.
The models were tested across multiple computer vision datasets for domain adaptation.
The dissertation concludes with a discussion on the proposed approaches and future directions for research in closed set and generalized domain adaptation.
This thesis provides solutions for the standard problem of unsupervised domain adaptation (UDA) and the more generic problem of generalized domain adaptation (GDA). The contributions of this thesis are as follows. (1) Certain and Consistent Domain Adaptation model for closed-set unsupervised domain adaptation by aligning the features of the source and target domain using deep neural networks. (2) A multi-adversarial deep learning model for generalized domain adaptation. (3) A gating model that detects out-of-distribution samples for generalized domain adaptation.
The models were tested across multiple computer vision datasets for domain adaptation.
The dissertation concludes with a discussion on the proposed approaches and future directions for research in closed set and generalized domain adaptation.
ContributorsNagabandi, Bhadrinath (Author) / Panchanathan, Sethuraman (Thesis advisor) / Venkateswara, Hemanth (Thesis advisor) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2020
Description
Societal infrastructure is built with vision at the forefront of daily life. For those with
severe visual impairments, this creates countless barriers to the participation and
enjoyment of life’s opportunities. Technological progress has been both a blessing and
a curse in this regard. Digital text together with screen readers and refreshable Braille
displays have made whole libraries readily accessible and rideshare tech has made
independent mobility more attainable. Simultaneously, screen-based interactions and
experiences have only grown in pervasiveness and importance, precluding many of
those with visual impairments.
Sensory Substituion, the process of substituting an unavailable modality with
another one, has shown promise as an alternative to accomodation, but in recent
years meaningful strides in Sensory Substitution for vision have declined in frequency.
Given recent advances in Computer Vision, this stagnation is especially disconcerting.
Designing Sensory Substitution Devices (SSDs) for vision for use in interactive settings
that leverage modern Computer Vision techniques presents a variety of challenges
including perceptual bandwidth, human-computer-interaction, and person-centered
machine learning considerations. To surmount these barriers an approach called Per-
sonal Foveated Haptic Gaze (PFHG), is introduced. PFHG consists of two primary
components: a human visual system inspired interaction paradigm that is intuitive
and flexible enough to generalize to a variety of applications called Foveated Haptic
Gaze (FHG), and a person-centered learning component to address the expressivity
limitations of most SSDs. This component is called One-Shot Object Detection by
Data Augmentation (1SODDA), a one-shot object detection approach that allows a
user to specify the objects they are interested in locating visually and with minimal
effort realizing an object detection model that does so effectively.
The Personal Foveated Haptic Gaze framework was realized in a virtual and real-
world application: playing a 3D, interactive, first person video game (DOOM) and
finding user-specified real-world objects. User study results found Foveated Haptic
Gaze to be an effective and intuitive interface for interacting with dynamic visual
world using solely haptics. Additionally, 1SODDA achieves competitive performance
among few-shot object detection methods and high-framerate many-shot object de-
tectors. The combination of which paves the way for modern Sensory Substitution
Devices for vision.
severe visual impairments, this creates countless barriers to the participation and
enjoyment of life’s opportunities. Technological progress has been both a blessing and
a curse in this regard. Digital text together with screen readers and refreshable Braille
displays have made whole libraries readily accessible and rideshare tech has made
independent mobility more attainable. Simultaneously, screen-based interactions and
experiences have only grown in pervasiveness and importance, precluding many of
those with visual impairments.
Sensory Substituion, the process of substituting an unavailable modality with
another one, has shown promise as an alternative to accomodation, but in recent
years meaningful strides in Sensory Substitution for vision have declined in frequency.
Given recent advances in Computer Vision, this stagnation is especially disconcerting.
Designing Sensory Substitution Devices (SSDs) for vision for use in interactive settings
that leverage modern Computer Vision techniques presents a variety of challenges
including perceptual bandwidth, human-computer-interaction, and person-centered
machine learning considerations. To surmount these barriers an approach called Per-
sonal Foveated Haptic Gaze (PFHG), is introduced. PFHG consists of two primary
components: a human visual system inspired interaction paradigm that is intuitive
and flexible enough to generalize to a variety of applications called Foveated Haptic
Gaze (FHG), and a person-centered learning component to address the expressivity
limitations of most SSDs. This component is called One-Shot Object Detection by
Data Augmentation (1SODDA), a one-shot object detection approach that allows a
user to specify the objects they are interested in locating visually and with minimal
effort realizing an object detection model that does so effectively.
The Personal Foveated Haptic Gaze framework was realized in a virtual and real-
world application: playing a 3D, interactive, first person video game (DOOM) and
finding user-specified real-world objects. User study results found Foveated Haptic
Gaze to be an effective and intuitive interface for interacting with dynamic visual
world using solely haptics. Additionally, 1SODDA achieves competitive performance
among few-shot object detection methods and high-framerate many-shot object de-
tectors. The combination of which paves the way for modern Sensory Substitution
Devices for vision.
ContributorsFakhri, Bijan (Author) / Panchanathan, Sethuraman (Thesis advisor) / McDaniel, Troy L (Committee member) / Venkateswara, Hemanth (Committee member) / Amor, Heni (Committee member) / Arizona State University (Publisher)
Created2020