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| Author | Marc Masana | ||||
| Title | Lifelong Learning of Neural Networks: Detecting Novelty and Adapting to New Domains without Forgetting | Type | Book Whole | ||
| Year | 2020 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Computer vision has gone through considerable changes in the last decade as neural networks have come into common use. As available computational capabilities have grown, neural networks have achieved breakthroughs in many computer vision tasks, and have even surpassed human performance in others. With accuracy being so high, focus has shifted to other issues and challenges. One research direction that saw a notable increase in interest is on lifelong learning systems. Such systems should be capable of efficiently performing tasks, identifying and learning new ones, and should moreover be able to deploy smaller versions of themselves which are experts on specific tasks. In this thesis, we contribute to research on lifelong learning and address the compression and adaptation of networks to small target domains, the incremental learning of networks faced with a variety of tasks, and finally the detection of out-of-distribution samples at inference time.
We explore how knowledge can be transferred from large pretrained models to more task-specific networks capable of running on smaller devices by extracting the most relevant information. Using a pretrained model provides more robust representations and a more stable initialization when learning a smaller task, which leads to higher performance and is known as domain adaptation. However, those models are too large for certain applications that need to be deployed on devices with limited memory and computational capacity. In this thesis we show that, after performing domain adaptation, some learned activations barely contribute to the predictions of the model. Therefore, we propose to apply network compression based on low-rank matrix decomposition using the activation statistics. This results in a significant reduction of the model size and the computational cost. Like human intelligence, machine intelligence aims to have the ability to learn and remember knowledge. However, when a trained neural network is presented with learning a new task, it ends up forgetting previous ones. This is known as catastrophic forgetting and its avoidance is studied in continual learning. The work presented in this thesis extensively surveys continual learning techniques and presents an approach to avoid catastrophic forgetting in sequential task learning scenarios. Our technique is based on using ternary masks in order to update a network to new tasks, reusing the knowledge of previous ones while not forgetting anything about them. In contrast to earlier work, our masks are applied to the activations of each layer instead of the weights. This considerably reduces the number of parameters to be added for each new task. Furthermore, the analysis on a wide range of work on incremental learning without access to the task-ID, provides insight on current state-of-the-art approaches that focus on avoiding catastrophic forgetting by using regularization, rehearsal of previous tasks from a small memory, or compensating the task-recency bias. Neural networks trained with a cross-entropy loss force the outputs of the model to tend toward a one-hot encoded vector. This leads to models being too overly confident when presented with images or classes that were not present in the training distribution. The capacity of a system to be aware of the boundaries of the learned tasks and identify anomalies or classes which have not been learned yet is key to lifelong learning and autonomous systems. In this thesis, we present a metric learning approach to out-of-distribution detection that learns the task at hand on an embedding space. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
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Ediciones Graficas Rey | Place of Publication | Editor | Joost Van de Weijer;Andrew Bagdanov | |
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| ISSN | ISBN | 978-84-121011-9-5 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ Mas20 | Serial | 3481 | ||
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| Author | Lei Kang | ||||
| Title | Robust Handwritten Text Recognition in Scarce Labeling Scenarios: Disentanglement, Adaptation and Generation | Type | Book Whole | ||
| Year | 2020 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Handwritten documents are not only preserved in historical archives but also widely used in administrative documents such as cheques and claims. With the rise of the deep learning era, many state-of-the-art approaches have achieved good performance on specific datasets for Handwritten Text Recognition (HTR). However, it is still challenging to solve real use cases because of the varied handwriting styles across different writers and the limited labeled data. Thus, both explorin a more robust handwriting recognition architectures and proposing methods to diminish the gap between the source and target data in an unsupervised way are
demanded. In this thesis, firstly, we explore novel architectures for HTR, from Sequence-to-Sequence (Seq2Seq) method with attention mechanism to non-recurrent Transformer-based method. Secondly, we focus on diminishing the performance gap between source and target data in an unsupervised way. Finally, we propose a group of generative methods for handwritten text images, which could be utilized to increase the training set to obtain a more robust recognizer. In addition, by simply modifying the generative method and joining it with a recognizer, we end up with an effective disentanglement method to distill textual content from handwriting styles so as to achieve a generalized recognition performance. We outperform state-of-the-art HTR performances in the experimental results among different scientific and industrial datasets, which prove the effectiveness of the proposed methods. To the best of our knowledge, the non-recurrent recognizer and the disentanglement method are the first contributions in the handwriting recognition field. Furthermore, we have outlined the potential research lines, which would be interesting to explore in the future. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher |
Ediciones Graficas Rey | Place of Publication | Editor | Alicia Fornes;Marçal Rusiñol;Mauricio Villegas | |
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| ISSN | ISBN | 978-84-122714-0-9 | Medium | ||
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| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ Kan20 | Serial | 3482 | ||
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| Author | Manuel Carbonell | ||||
| Title | Neural Information Extraction from Semi-structured Documents A | Type | Book Whole | ||
| Year | 2020 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Sectors as fintech, legaltech or insurance process an inflow of millions of forms, invoices, id documents, claims or similar every day. Together with these, historical archives provide gigantic amounts of digitized documents containing useful information that needs to be stored in machine encoded text with a meaningful structure. This procedure, known as information extraction (IE) comprises the steps of localizing and recognizing text, identifying named entities contained in it and optionally finding relationships among its elements. In this work we explore multi-task neural models at image and graph level to solve all steps in a unified way. While doing so we find benefits and limitations of these end-to-end approaches in comparison with sequential separate methods. More specifically, we first propose a method to produce textual as well as semantic labels with a unified model from handwritten text line images. We do so with the use of a convolutional recurrent neural model trained with connectionist temporal classification to predict the textual as well as semantic information encoded in the images. Secondly, motivated by the success of this approach we investigate the unification of the localization and recognition tasks of handwritten text in full pages with an end-to-end model, observing benefits in doing so. Having two models that tackle information extraction subsequent task pairs in an end-to-end to end manner, we lastly contribute with a method to put them all together in a single neural network to solve the whole information extraction pipeline in a unified way. Doing so we observe some benefits and some limitations in the approach, suggesting that in certain cases it is beneficial to train specialized models that excel at a single challenging task of the information extraction process, as it can be the recognition of named entities or the extraction of relationships between them. For this reason we lastly study the use of the recently arrived graph neural network architectures for the semantic tasks of the information extraction process, which are recognition of named entities and relation extraction, achieving promising results on the relation extraction part. | ||||
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher |
Ediciones Graficas Rey | Place of Publication | Editor | Alicia Fornes;Mauricio Villegas;Josep Llados | |
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| ISSN | ISBN | 978-84-122714-1-6 | Medium | ||
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| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ Car20 | Serial | 3483 | ||
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| Author | Gemma Rotger | ||||
| Title | Lifelike Humans: Detailed Reconstruction of Expressive Human Faces | Type | Book Whole | ||
| Year | 2021 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Developing human-like digital characters is a challenging task since humans are used to recognizing our fellows, and find the computed generated characters inadequately humanized. To fulfill the standards of the videogame and digital film productions it is necessary to model and animate these characters the most closely to human beings. However, it is an arduous and expensive task, since many artists and specialists are required to work on a single character. Therefore, to fulfill these requirements we found an interesting option to study the automatic creation of detailed characters through inexpensive setups. In this work, we develop novel techniques to bring detailed characters by combining different aspects that stand out when developing realistic characters, skin detail, facial hairs, expressions, and microexpressions. We examine each of the mentioned areas with the aim of automatically recover each of the parts without user interaction nor training data. We study the problems for their robustness but also for the simplicity of the setup, preferring single-image with uncontrolled illumination and methods that can be easily computed with the commodity of a standard laptop. A detailed face with wrinkles and skin details is vital to develop a realistic character. In this work, we introduce our method to automatically describe facial wrinkles on the image and transfer to the recovered base face. Then we advance to facial hair recovery by resolving a fitting problem with a novel parametrization model. As of last, we develop a mapping function that allows transfer expressions and microexpressions between different meshes, which provides realistic animations to our detailed mesh. We cover all the mentioned points with the focus on key aspects as (i) how to describe skin wrinkles in a simple and straightforward manner, (ii) how to recover 3D from 2D detections, (iii) how to recover and model facial hair from 2D to 3D, (iv) how to transfer expressions between models holding both skin detail and facial hair, (v) how to perform all the described actions without training data nor user interaction. In this work, we present our proposals to solve these aspects with an efficient and simple setup. We validate our work with several datasets both synthetic and real data, prooving remarkable results even in challenging cases as occlusions as glasses, thick beards, and indeed working with different face topologies like single-eyed cyclops. | ||||
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| Corporate Author | Thesis | Ph.D. thesis | |||
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Ediciones Graficas Rey | Place of Publication | Editor | Felipe Lumbreras;Antonio Agudo | |
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| ISSN | ISBN | 978-84-122714-3-0 | Medium | ||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ Rot2021 | Serial | 3513 | ||
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| Author | Ramon Baldrich | ||||
| Title | Perceptual approach to a computational colour-texture representation for surface inspection. | Type | Book Whole | ||
| Year | 2001 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Notes | CIC | Approved | no | ||
| Call Number | CAT @ cat @ Bal2001 | Serial | 73 | ||
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| Author | Ricardo Toledo | ||||
| Title | Cardiac workstation and dynamic model to assist in coronary tree analysis. | Type | Book Whole | ||
| Year | 2001 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Corporate Author | Thesis | Ph.D. thesis | |||
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Place of Publication | Editor | Petia Radeva;JuanJose Villanueva | ||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ Tol2001 | Serial | 166 | ||
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| Author | Antonio Lopez | ||||
| Title | Multilocal Methods for Ridge and Valley Delineation in Image Analysis. | Type | Book Whole | ||
| Year | 2000 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Place of Publication | Editor | Joan Serrat | ||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ Lop2000 | Serial | 174 | ||
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| Author | Felipe Lumbreras | ||||
| Title | Segmentation, classification and modelization of textures by means of multiresolution decomposition techniques. | Type | Book Whole | ||
| Year | 2001 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ Lum2001 | Serial | 188 | ||
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| Author | A. Pujol | ||||
| Title | Contributions to shape and texture face similarity measurement. | Type | Book Whole | ||
| Year | 2001 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Place of Publication | Editor | JuanJose Villanueva | ||
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ Puj2001 | Serial | 202 | ||
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| Author | David Lloret | ||||
| Title | Medical Image Registration Based on a Creaseress Measure. | Type | Book Whole | ||
| Year | 2002 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Corporate Author | Thesis | Ph.D. thesis | |||
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Place of Publication | Editor | Joan Serrat | ||
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ Llo2002 | Serial | 321 | ||
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| Author | Jordi Gonzalez | ||||
| Title | Human Sequence Evaluation: the Key-frame Approach | Type | Book Whole | ||
| Year | 2004 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Corporate Author | Thesis | Ph.D. thesis | |||
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Place of Publication | Editor | Xavier Roca;Javier Varona | ||
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| Notes | Approved | no | |||
| Call Number | ISE @ ise @ Gon2004 | Serial | 362 | ||
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| Author | David Guillamet | ||||
| Title | Statistical Local Appearance Models for Object Recognition | Type | Book Whole | ||
| Year | 2004 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Address | Bellaterra | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
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Place of Publication | Editor | Jordi Vitria | ||
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ Gui2004 | Serial | 444 | ||
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| Author | Oriol Pujol | ||||
| Title | A semi-Supervised Statistical Framework and Generative Snakes for IVUS Analysis | Type | Book Whole | ||
| Year | 2004 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Address | CVC (UAB), Bellaterra | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
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Place of Publication | Editor | Petia Radeva | ||
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| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ Puj2004 | Serial | 512 | ||
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| Author | David Masip | ||||
| Title | Face Classification Using Discriminative Features and Classifier Combination | Type | Book Whole | ||
| Year | 2005 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
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Place of Publication | Editor | Jordi Vitria | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 84-933652-3-8 | Medium | ||
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| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ Mas2005b | Serial | 602 | ||
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| Author | Misael Rosales | ||||
| Title | A Physics-Based Image Modelling of IVUS as a Geometric and Kinematic System | Type | Book Whole | ||
| Year | 2005 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher |
Place of Publication | Editor | Petia Radeva | ||
| Language | Summary Language | Original Title | |||
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| Area | Expedition | 978-84-922529-8-7 | Conference | ||
| Notes | Approved | no | |||
| Call Number | Admin @ si @ Ros2005 | Serial | 603 | ||
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