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 20 of 105 for “"Self-Supervised Learning"”.
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Federated self-supervised learning
Federated learning (FL) has garnered significant attention from both research and industrial communities due to its distinctive ability to facilitate collaborative learning from large-scale datasets without compromising users’ data privacy. However, current FL practices predominantly focus on …
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Self-Supervised Learning for Geometry
… we cast the geometric problems as machine learning problems, specifically, deep learning problems. Differ from conventional supervised learning methods that using expensive annotations as the supervisory signal, we advocate for the use of geometry as a supervisory signal to improve the …
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Self-Supervised Learning for Speech Processing
Deep neural networks trained with supervised learning algorithms on large amounts of labeled speech data have achieved remarkable performance on various spoken language processing applications, often being the state of the arts on the corresponding leaderboards. However, the fact that training …
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Visual Domain Generalization via Self-Supervised Learning
L'abstract è presente nell'allegato / the abstract is in the attachment
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Self-supervised learning frameworks for IoT applications
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms
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Structuring Representation Geometry in Self-Supervised Learning
The central promise of deep learning is to learn a map 𝑓 : 𝒳 → ℝ_𝑑 that transforms objects 𝒳—represented in their raw perceptual forms, such as images or molecular strings—into a representation space ℝ_𝑑 where everything that is hard to do with raw perceptual data becomes easy. For instance, …
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Self-supervised Learning Methods for Vision-based Tasks
… to leverage this data for training many machine learning models. Among them, self-supervised learning appears as an efficient solution capable of training powerful and generalizable models. More specifically, instead of relying on human-generated labels, it proposes training objectives that use …
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Self-supervised Learning of Monocular Depth from Video
… problem of monocular depth estimation via self-supervised learning from RGB-only videos. Although existing work has shown partial excellent results in benchmark datasets, there remain several vital challenges that limit the use of these algorithms in general scenarios. To summarize, my …
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Self-supervised Learning for IMU-based Human Activity Recognition
… units. In this thesis, we propose the use of self-supervised learning for human activity recognition using the tri-axial data collected from the smartphone-embedded accelerometers. To address the limitations of fully-supervised learning, mainly reliance on labeled data, we propose two …
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Self-supervised learning of spatiotemporal features from video colorization
The student, Zubin Pahuja, accepted the attached license on 2019-07-19 at 13:10.
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Self-supervised learning for data-efficient human activity recognition
… actions. Motivated by advancements in deep learning, human activity recognition research has also widely adopted these methods. However, compared to other data modalities, human activity recognition models struggle with the limited availability of labels, due to the difficulty of …
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Classifying and Displaying Brain-waves through Self-supervised Learning
… Hence, constructing labeled datasets for supervised learning from EEG signals is expensive and time-consuming. Moreover, the existing datasets use incompatible EEG setups (e.g. different numbers of channels, sampling rates, types of sensors, etc.) that make them hard to fuse to obtain …
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Multimodal dynamics : self-supervised learning in perceptual and motor systems
This thesis presents a self-supervised framework for perceptual and motor learning based upon correlations in different sensory modalities. The brain and cognitive sciences have gathered an enormous body of neurological and phenomenological evidence in the past half century demonstrating the …
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Enhancing Self-Supervised Learning through Transformations in Higher Activation Space
… through extensive experiments on contrastive learning tasks in computer vision and NLP domains, where we observe substantial performance gains with ResNets and Transformers as the underlying models. Our experimentation reveals that targeting deeper layers with Deep Augmentation outperforms …
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Dataset and Evaluation of Self-Supervised Learning for Panoramic Depth Estimation
… clouds. One of the poster child applications is self driving cars. Currently, the best methods for depth detection are either very expensive, like LIDAR, or require precise calibration, like stereo cameras. These costs have given rise to attempts to detect depth from a monocular camera (a single …
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Self-Supervised Learning Method for Semantic Segmentation of LiDAR Point Clouds
… LiDAR point clouds, largely the adopting of deep learning techniques. There are the related works of 3D semantic segmentation, including neural network models to process converted voxels, points, and graphs. However, point-based methods are not taken into account local structure feature, resulting …
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"Towards Closed-Loop Sleep Monitoring in Parkinson’s Disease: Self-Supervised Learning Strategies for Sleep Stage Classification"
… generalization, this work introduces a self-supervised transformer framework. The model employs a masked autoencoder strategy, pretrained on large public EEG/ECoG datasets from healthy subjects to learn balanced representations of all sleep stages, thereby improving discrimination of …
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