Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 12 of 12 for “"Semi-supervised training"”.
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Semi-supervised learning for acoustic and prosodic modeling in speech applications
… transcribed (labeled) data. We propose a unified semi-supervised learning framework for the problem of phone classification, phone recognition and prosody detection. The proposed approach will be particularly useful in the case where recognition performance is limited by the amount of transcribed …
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Monocular Visual Inertial Odometry using Learning-based Methods
… KITTI and EuROC, and a custom dataset using supervised, unsupervised and semi-supervised training methods. Compared to traditional methods, the deep-learning methods presented here do not require precise manual synchronization of the camera and IMU or explicit camera calibration.</p> <p>To …
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Unsupervised Text Translation Through the Application of Generative Adversarial Networks
… these problems is the lack of access to paired training data, which inhibits training via a straightforward maximum likelihood estimation approach. This requires us to focus on unsupervised techniques for text translation that depend only on access to large domains of unpaired data. For …
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Semi-supervised cycle-consistency training for end-to-end ASR using unpaired speech
… and computer vision. In ASR, cycle-consistency training is achieved by building a reverse system, e.g., a text-to-speech system, and designing a loss based on the reconstructed signal and the original one. However, it is not straightforward to apply cycle-consistency in ASR as information would …
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Multiple Choice Question Answering using a Large Corpus of Information
… and general information from a large corpus in a semi-supervised setting. In Chapter 4 I propose a strategy to train a network to simultaneously classify multiple choice questions and learn to generate words relevant to the surrounding context of the question. Using the Transformer architecture in …
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Semi-supervised and active training of conditional random fields for activity recognition
… time, this thesis explores the application of semi-supervised and active learning in activity recognition. We present a new and efficient semi-supervised training method for parameter estimation and feature selection in conditional random fields (CRFs),a probabilistic graphical model. In …
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Image-Based Spatial Change Detection Using Deep Learning
… great representational capacity, straightforward training, and state-of-the-art performance in visual tasks. Nevertheless, CNNs, like most DL approaches, still face limitations relating to their reliance on extensive labelled datasets, the localization accuracy and detail of the predicted outputs, …
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Dealing with Inaccurate and Incomplete Labels in Industrial Streaming Data
… in the embedding space. They allow for robust semi-supervised training of deep neural networks in the presence of label noise, and even gradually correct the mislabeled samples during training. Similarly, connecting these latent representations to a network performing predefined tasks is shown …
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Self-supervised learning for data-efficient human activity recognition
… challenges by developing and evaluating novel training paradigms. Our proposed paradigms leverage data from additional sources, including other devices and readily available unlabelled data that can be collected easily and often passively, to provide supervision for deep learning, enabling …
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A Large Collection Learning Optimizer Framework
… around the most discriminating words in the training data. The hierarchy of rules, along with an ability to tune to a support threshold, makes it an effective classifier for scenarios where short text is involved. Traditionally, developing classification systems for these purposes requires a …
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Attention-Based Encoder-Decoder Models for Speech Processing
… With various advancements in neural network training, AED models can reach similar or better performance than traditional systems for many ASR tasks. Compared to a conventional ASR system, one important but perhaps missing attribute of an AED-based system is good confidence scores which …
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Active and Semi-Supervised Learning for Speech Recognition
… The increase in computing power enabled the training of models on ever-expanding data sets, and deep learning allowed for the better exploitation of these large data sets. For commercial products, training on multiple thousands of hours of transcribed audio is common practice. However, the …