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.
Results
Showing 1 to 5 of 5 for “"Capsule Network"”.
-
AUTOMATIC IDENTIFICATION OF ANIMALS IN THE WILD: A COMPARATIVE STUDY BETWEEN C-CAPSULE NETWORKS AND DEEP CONVOLUTIONAL NEURAL NETWORKS.
… is also an opportunity to present some custom Capsule Networks architectures to the deep learning community while solving the above-mentioned critical problem. Incidentally, we are going to take advantage of these data to make a comparative study on multiple deep learning models, specifically, …
-
Application of capsule networks for image classification on complex datasets
Capsule Network, introduced in 2017 by Sabour, Hinton, and Frost, has sparked great interest in the computer vision and deep learning community and offers a paradigm shift in neural computation. In CapsNet, Sabour et. al. replace classical notions of scalar neural computation with a vectorised …
-
Deep Representation Learning for Speaker Recognition
… first challenge, we proposed a new deep neural network, UtterIdNet which is capable of achieving strong SR results with short speech segments especially sub-second durations (250 ms and 500 ms). For the second goal of this thesis, we proposed FEFA which is capable of focusing on information …
-
Computer Vision Applications for Autonomous Aerial Vehicles
… including a depth camera. Next, a modified capsule network for 3D object classification is presented with weight optimization so that the network can be fit and run on memory-constrained platforms. Then, a semantic segmentation method for 3D point clouds is developed for a more general …
-
EEG-based Brain Computer Interface with Deep Learning
… we propose a Long Short-Term Memory (LSTM) network with an attention mechanism to learn the importance of EEG information varying through time, where discriminative information with higher importance is assigned higher scores to better contribute to the classification performance. Our model …