883 citations · 2.5k across the 44 of their papers we have counts for
9 papers · 1 filter
Explaining Deep Convolutional Neural Networks for Image Classification by Evolving Local Interpretable Model-agnostic Explanations
Bin Wang, Wenbin Pei, Bing Xue +1
Deep convolutional neural networks have proven their effectiveness, and have been acknowledged as the most dominant method for image classification. However, a severe drawback of d…
Deep Learning Model with GA based Feature Selection and Context Integration
Ranju Mandal, Basim Azam, Brijesh Verma +1
Deep learning models have been very successful in computer vision and image processing applications. Since its inception, Many top-performing methods for image segmentation are bas…
Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning
Ying Bi, Bing Xue, Mengjie Zhang
Using evolutionary computation algorithms to solve multiple tasks with knowledge sharing is a promising approach. Image feature learning can be considered as a multitask problem be…
Surrogate-assisted Particle Swarm Optimisation for Evolving Variable-length Transferable Blocks for Image Classification
Bin Wang, Bing Xue, Mengjie Zhang
Deep convolutional neural networks have demonstrated promising performance on image classification tasks, but the manual design process becomes more and more complex due to the fas…
A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks
Bin Wang, Yanan Sun, Bing Xue +1
Image classification is a difficult machine learning task, where Convolutional Neural Networks (CNNs) have been applied for over 20 years in order to solve the problem. In recent y…
Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation
Muhammad Ghifary, W. Bastiaan Kleijn, Mengjie Zhang +2
In this paper, we propose a novel unsupervised domain adaptation algorithm based on deep learning for visual object recognition. Specifically, we design a new model called Deep Rec…
Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization
Muhammad Ghifary, David Balduzzi, W. Bastiaan Kleijn +1
This paper addresses classification tasks on a particular target domain in which labeled training data are only available from source domains different from (but related to) the ta…
Domain Generalization for Object Recognition with Multi-task Autoencoders
Muhammad Ghifary, W. Bastiaan Kleijn, Mengjie Zhang +1
The problem of domain generalization is to take knowledge acquired from a number of related domains where training data is available, and to then successfully apply it to previousl…
Domain Adaptive Neural Networks for Object Recognition
Muhammad Ghifary, W. Bastiaan Kleijn, Mengjie Zhang
We propose a simple neural network model to deal with the domain adaptation problem in object recognition. Our model incorporates the Maximum Mean Discrepancy (MMD) measure as a re…