Search Results for author:
Found 16 papers, 1 papers with code
Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks
In many practical few-shot learning problems, even though labeled examples are scarce, there are abundant auxiliary datasets that potentially contain useful information.
Classification of Diabetic Retinopathy Using Unlabeled Data and Knowledge Distillation
Knowledge distillation addresses some of the shortcomings associated with transfer learning by generalizing a complex model to a lighter model.
Acceleration of Convolutional Neural Network Using FFT-Based Split Convolutions
In this paper, a new method for CNN processing in the FFT domain is proposed, which is based on input splitting.
Convolutional Neural Network Pruning Using Filter Attenuation
In the proposed attenuation approach, weak filters are not abruptly removed, and there is a chance for these filters to return to the network.
Unlabeled Data Deployment for Classification of Diabetic Retinopathy Images Using Knowledge Transfer
Knowledge distillation is recently proposed to transfer the knowledge of a model to another one and can be useful to cover the shortcomings of transfer learning.
Splitting Convolutional Neural Network Structures for Efficient Inference
For convolutional neural networks (CNNs) that have a large volume of input data, memory management becomes a major concern.
Modeling of Pruning Techniques for Deep Neural Networks Simplification
In this paper pruning methods are investigated, and a general model which is contained the majority of pruning techniques is proposed.
Modeling Neural Architecture Search Methods for Deep Networks
There are many research works on the designing of architectures for the deep neural networks (DNN), which are named neural architecture search (NAS) methods.
Modeling Teacher-Student Techniques in Deep Neural Networks for Knowledge Distillation
By utilizing the proposed model, different methods in KD are better investigated and explored.
Multiple Abnormality Detection for Automatic Medical Image Diagnosis Using Bifurcated Convolutional Neural Network
In recent years portable medical imaging devices such as capsule endoscopy and digital dermatoscope have been introduced and made the diagnosis procedure easier and more efficient.
Segmentation of Bleeding Regions in Wireless Capsule Endoscopy for Detection of Informative Frames
Both CNN and MLP structures are simplified to reduce the number of computational operations.
Lossless Compression of Angiogram Foreground with Visual Quality Preservation of Background
By increasing the volume of telemedicine information, the need for medical image compression has become more important.
Reversible Image Watermarking for Health Informatics Systems Using Distortion Compensation in Wavelet Domain
In this study a reversible watermarking is investigated with focusing on increasing the embedding capacity and reducing the distortion in medical images.
Segmentation of Bleeding Regions in Wireless Capsule Endoscopy Images an Approach for inside Capsule Video Summarization
Wireless capsule endoscopy (WCE) is an effective means of diagnosis of gastrointestinal disorders.
Context aware saliency map generation using semantic segmentation
In this paper a saliency map is proposed, based on image context detection using semantic segmentation as a high level feature.
Adaptive Real-Time Removal of Impulse Noise in Medical Images
In this paper a low complexity de-noising method is proposed that removes the noise by local analysis of the image blocks.
