A survey on deep neural network compression: Challenges, overview, and solutions
Deep Neural Network (DNN) has gained unprecedented performance due to its automated
feature extraction capability. This high order performance leads to significant incorporation …
feature extraction capability. This high order performance leads to significant incorporation …
Transforming large-size to lightweight deep neural networks for IoT applications
Deep Neural Networks (DNNs) have gained unprecedented popularity due to their high-
order performance and automated feature extraction capability. This has encouraged …
order performance and automated feature extraction capability. This has encouraged …
Multitask-centernet (mcn): Efficient and diverse multitask learning using an anchor free approach
F Heuer, S Mantowsky, S Bukhari… - Proceedings of the …, 2021 - openaccess.thecvf.com
Multitask learning is a common approach in machine learning, which allows to train multiple
objectives with a shared architecture. It has been shown that by training multiple tasks …
objectives with a shared architecture. It has been shown that by training multiple tasks …
[HTML][HTML] Two-speed deep-learning ensemble for classification of incremental land-cover satellite image patches
High-velocity data streams present a challenge to deep learning-based computer vision
models due to the resources needed to retrain for new incremental data. This study presents …
models due to the resources needed to retrain for new incremental data. This study presents …
Distilling knowledge from an ensemble of convolutional neural networks for seismic fault detection
Z Wang, B Li, N Liu, B Wu, X Zhu - IEEE Geoscience and …, 2020 - ieeexplore.ieee.org
Fault detection is a crucial task in seismic structure interpretation. Convolutional neural
network (CNN)-based methods, in general, require large amount of labeled data for network …
network (CNN)-based methods, in general, require large amount of labeled data for network …
Advances and challenges in deep lip reading
M Oghbaie, A Sabaghi, K Hashemifard… - arXiv preprint arXiv …, 2021 - arxiv.org
Driven by deep learning techniques and large-scale datasets, recent years have witnessed
a paradigm shift in automatic lip reading. While the main thrust of Visual Speech …
a paradigm shift in automatic lip reading. While the main thrust of Visual Speech …
Classification of diabetic retinopathy using unlabeled data and knowledge distillation
Over the last decade, advances in Machine Learning and Artificial Intelligence have
highlighted their potential as a diagnostic tool in the healthcare domain. Despite the …
highlighted their potential as a diagnostic tool in the healthcare domain. Despite the …
Self-supervised pretraining enables high-performance chest X-ray interpretation across clinical distributions
Chest X-rays (CXRs) are a rich source of information for physicians–essential for disease
diagnosis and treatment selection. Recent deep learning models aim to alleviate strain on …
diagnosis and treatment selection. Recent deep learning models aim to alleviate strain on …
EDANAS: adaptive neural architecture search for early exit neural networks
M Gambella, M Roveri - 2023 International Joint Conference on …, 2023 - ieeexplore.ieee.org
Early Exit Neural Networks (EENNs) endow neural network architectures with auxiliary
classifiers to progressively process the input and make decisions at intermediate points of …
classifiers to progressively process the input and make decisions at intermediate points of …
[PDF][PDF] Does redundancy in AI perception systems help to test for super-human automated driving performance?
H Gottschalk, M Rottmann… - Deep Neural Networks and …, 2022 - library.oapen.org
While automated driving is often advertised with better-than-human driving performance, this
chapter reviews that it is nearly impossible to provide direct statistical evidence on the …
chapter reviews that it is nearly impossible to provide direct statistical evidence on the …