These deep learning models were trained and evaluated on two large EEG seizure datasets in a person-specific manner. We found that both supervised and unsupervised approaches are feasible; however, their performance varies depending on the patient, approach and architecture. This new line of research...
Among them, deep learning (DL) approaches have emerged as the most effective, due to their robust feature extraction capabilities. Yet, the prevalent use of supervised training paradigms is often impractical due to the challenges in acquiring low-dose and normal-dose CT pairs in clinical settings...
Propensity score matching is vital in epidemiological studies using observational data, yet its estimates relies on correct model-specification. This study assesses supervised deep learning models and unsupervised autoencoders for propensity score estimation, comparing them with traditional methods for bias ...
They include supervised and unsupervisedapproaches. Accuracy assessment of a remote sensing output is a most important step in classification of remotely sensed data. Without accuracy assessment the quality of map or output produced would be of lesser value to the end user. However, supervised and ...
Although ANN is used for both supervised and unsupervised learning applications [97], their application for supervised ML is much more common for ICEs. ANN has been used extensively for modeling and control of ICEs [98–109]. These applications include: i) predicting engine performance metrics (...
In fact, videos of the same task often have similar tran- scripts. For example, in videos of 'make fried-egg', the step of 'cracking egg' is followed by 'frying egg'. While such similar ordering has been the key in the develop- ment and success ...
To address these challenges, a semi-supervised learning network for deep-sea nodule image segmentation (NmiNet) was proposed. In this method, a semi-supervised training paradigm based on underwater image enhancement perturbation and uncertainty weighted optimization (UEUO) was designed. This paradigm ...
In supervised learning, we know the right answer beforehand when we train our model, and in reinforcement learning, we define a measure of reward for particular actions by the agent. In unsupervised learning, however, we are dealing with unlabeled data or data of unknown structure. Using ...
Because semi-supervised learning requires less human effort and gives higher accuracy, it is of great interest both in theory and in practice. How many semi-supervised learning methods are there? Many. Some often-used methods include: EM with generative mixture models, self-training, consistency ...
Exploring Self-Supervised Learning for Multi-Modal Remote Sensing Pre-Training via Asymmetric Attention Fusion bridged the gap between supervised and unsupervised learning in computer vision tasks and shown impressive success in the field of remote sensing (RS). How... G Xu,X Jiang,X Li,... -...