Then, we evaluate and compare the performance of the explainable models derived from UNISAL, MSI-Net and CMRNet on three benchmark datasets with other state-of-the-art methods.
We have used eye-tracking to assay the spatial distribution of information hotspots for humans via fixation maps and an activation mapping technique for obtaining analogous distributions for deep networks through visualization maps.
Firstly, we present the COVID-19 Multi-Task Network which is an automated end-to-end network for COVID-19 screening.
Fall detection holds immense importance in the field of healthcare, where timely detection allows for instant medical assistance.
Data abundance along with scarcity of machine learning experts and domain specialists necessitates progressive automation of end-to-end machine learning workflows.
In this paper, we present a novel end-to-end coupled Denoising based Saliency Prediction with Generative Adversarial Network (DSAL-GAN) framework to address the problem of salient object detection in noisy images.
Generative adversarial networks (GANs) have shown remarkable success in generation of unstructured data, such as, natural images.
Conditional generation refers to the process of sampling from an unknown distribution conditioned on semantics of the data.
These approaches require object-centric images to perform matching.
Quantification of physiological changes in plants can capture different drought mechanisms and assist in selection of tolerant varieties in a high throughput manner.
Training deep networks is expensive and time-consuming with the training period increasing with data size and growth in model parameters.
The crux of the problem in KDD Cup 2016 involves developing data mining techniques to rank research institutions based on publications.
In this paper we propose to apply CNN to small data sets like for example, personal albums or other similar environs where the size of training dataset is a limitation, within the framework of a proposed hybrid CNN-AIS model.
This paper proposes a hybrid text recognizer using a deep recurrent neural network with multiple layers of abstraction and long range context along with a language model to verify the performance of the deep neural network.
Communication Service Providers (CSPs) are in a unique position to utilize their vast transactional data assets generated from interactions of subscribers with network elements as well as with other subscribers.
While computing similarity between users, we make use of a combined similarity measure involving rating overlap as well as similarity in the latent topic space.