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Found 71 papers, 17 papers with code
Deep Industrial Image Anomaly Detection: A Survey
In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets.
EvoX: A Distributed GPU-accelerated Framework for Scalable Evolutionary Computation
Inspired by natural evolutionary processes, Evolutionary Computation (EC) has established itself as a cornerstone of Artificial Intelligence.
Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment
From an optimization point of view, the NAS tasks involving multiple design criteria are intrinsically multiobjective optimization problems; hence, it is reasonable to adopt evolutionary multiobjective optimization (EMO) algorithms for tackling them.
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
We realize that the lack of a uniform IM benchmark is hindering the development and usage of IAD methods in real-world applications.
A Survey of Visual Sensory Anomaly Detection
In this survey, we are the first one to provide a comprehensive review of visual sensory AD and category into three levels according to the form of anomalies.
Evolutionary Dynamic Optimization Laboratory: A MATLAB Optimization Platform for Education and Experimentation in Dynamic Environments
In this paper, to assist researchers in performing experiments and comparing their algorithms against several EDOAs, we develop an open-source MATLAB platform for EDOAs, called Evolutionary Dynamic Optimization LABoratory (EDOLAB).
Ultralightweight Spatial–Spectral Feature Cooperation Network for Change Detection in Remote Sensing Images
First, the existing multiscale feature fusion methods often use redundant feature extraction and fusion strategies, which often lead to high computational costs and memory usage.
Ranked #2 on
Change Detection
on DSIFN-CD
Building change detection for remote sensing images
Change Detection
+1
Deep Learning Based Brain Tumor Segmentation: A Survey
Brain tumor segmentation is one of the most challenging problems in medical image analysis.
Ternary Compression for Communication-Efficient Federated Learning
Learning over massive data stored in different locations is essential in many real-world applications.
Information Maximization Clustering via Multi-View Self-Labelling
Specifically, the proposed clustering objective employs mutual information, and maximizes the dependency between the integrated discrete representation and a discrete probability distribution.
Ranked #2 on
Image Clustering
on Tiny-ImageNet
A Federated Data-Driven Evolutionary Algorithm
Data-driven evolutionary optimization has witnessed great success in solving complex real-world optimization problems.
FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis
There is a clear need to launch a federated learning and facilitate the integration of the dispersed data from different institutions.
FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss
The proposed method demonstrates the advanced performance in both the quality of our synthesized results under a severely misaligned and unpaired data setting, and better stability than other GAN-based algorithms.
Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search
Then, we propose multi-objective genetic programming (MOGP) to explore the NAS task's search space by maximizing model performance and interpretability.
A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimization
Data-driven optimization has found many successful applications in the real world and received increased attention in the field of evolutionary optimization.
Image Clustering using an Augmented Generative Adversarial Network and Information Maximization
Image clustering has recently attracted significant attention due to the increased availability of unlabelled datasets.
EMT-NAS:Transferring Architectural Knowledge Between Tasks From Different Datasets
In deep learning, this is usually achieved by sharing a common neural network architecture and jointly training the weights.
Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results
In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously.
An Efficient Method for online Detection of Polychronous Patterns in Spiking Neural Network
This work presents a new model of polychronous patterns that can capture precise sequences of spikes directly in the neural simulation.
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization
To address these issues, we have developed a MATLAB platform for evolutionary multi-objective optimization in this paper, called PlatEMO, which includes more than 50 multi-objective evolutionary algorithms and more than 100 multi-objective test problems, along with several widely used performance indicators.
A Particle Filter based Multi-Objective Optimization Algorithm: PFOPS
To this end, we make an effort to extend the scope of application of the PFO paradigm to multi-objective optimization (MOO) cases.
Multi-objective Evolutionary Federated Learning
A scalable method for encoding network connectivity is adapted to federated learning to enhance the efficiency in evolving deep neural networks.
Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation
The proposed algorithm is empirically on two datasets with different deep neural networks.
Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks
Recently, more and more works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i. e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality. To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into \emph{real} and \emph{fake} samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables. Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm.
Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks (GANs)
The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables.
Identification of Interaction Clusters Using a Semi-supervised Hierarchical Clustering Method
Motivation: Identifying interaction clusters of large gene regulatory networks (GRNs) is critical for its further investigation, while this task is very challenging, attributed to data noise in experiment data, large scale of GRNs, and inconsistency between gene expression profiles and function modules, etc.
CMSN: Continuous Multi-stage Network and Variable Margin Cosine Loss for Temporal Action Proposal Generation
Accurately locating the start and end time of an action in untrimmed videos is a challenging task.
A Survey of Deep Learning Applications to Autonomous Vehicle Control
However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios.
Real-time Federated Evolutionary Neural Architecture Search
Federated learning is a distributed machine learning approach to privacy preservation and two major technical challenges prevent a wider application of federated learning.
Hybrid Attention-Based Transformer Block Model for Distant Supervision Relation Extraction
With an exponential explosive growth of various digital text information, it is challenging to efficiently obtain specific knowledge from massive unstructured text information.
Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search
Recently, evolutionary neural architecture search (ENAS) has received increasing attention due to the attractive global optimization capability of evolutionary algorithms.
Generation of Consistent Sets of Multi-Label Classification Rules with a Multi-Objective Evolutionary Algorithm
Recently, the interest in interpretable classification models has grown, partially as a consequence of regulations such as the General Data Protection Regulation.
From Federated Learning to Federated Neural Architecture Search: A Survey
Federated learning is a recently proposed distributed machine learning paradigm for privacy preservation, which has found a wide range of applications where data privacy is of primary concern.
Distributed, Parallel, and Cluster Computing
Incremental Data-driven Optimization of Complex Systems in Nonstationary Environments
Existing work on data-driven optimization focuses on problems in static environments, but little attention has been paid to problems in dynamic environments.
Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification
First, based on the prototypical networks, we propose an adaptive mixture mechanism to add label words to the representation of the class prototype, which, to the best of our knowledge, is the first attempt to integrate the label information into features of the support samples of each class so as to get more interactive class prototypes.
Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks
Many existing deep learning models are vulnerable to adversarial examples that are imperceptible to humans.
Tiny Adversarial Mulit-Objective Oneshot Neural Architecture Search
Our work focuses on how to improve the robustness of tiny neural networks without seriously deteriorating of clean accuracy under mobile-level resources.
Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics
In the past three decades, a large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems.
Federated Learning on Non-IID Data: A Survey
Federated learning is an emerging distributed machine learning framework for privacy preservation.
PIVODL: Privacy-preserving vertical federated learning over distributed labels
Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data.
Transfer Learning Based Co-surrogate Assisted Evolutionary Bi-objective Optimization for Objectives with Non-uniform Evaluation Times
Most existing multiobjetive evolutionary algorithms (MOEAs) implicitly assume that each objective function can be evaluated within the same period of time.
FEVERLESS: Fast and Secure Vertical Federated Learning based on XGBoost for Decentralized Labels
Vertical Federated Learning (VFL) enables multiple clients to collaboratively train a global model over vertically partitioned data without revealing private local information.
A Generic Self-Supervised Framework of Learning Invariant Discriminative Features
Specifically, the prior transformation functions are replaced with a self-transformation mechanism, derived through an unsupervised training process of adversarial training, for imposing invariant representations.
Cross-Modality Neuroimage Synthesis: A Survey
This is followed by a stepwise in-depth analysis to evaluate how cross-modality neuroimage synthesis improves the performance of its downstream tasks.
A Survey on Computationally Efficient Neural Architecture Search
Neural architecture search (NAS) has become increasingly popular in the deep learning community recently, mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep neural networks (DNNs).
Recent Advances in Bayesian Optimization
Bayesian optimization has emerged at the forefront of expensive black-box optimization due to its data efficiency.
Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures
First, we formulate the NAS problem for enhancing adversarial robustness of deep neural networks into a multiobjective optimization problem.
Towards Fairness-Aware Multi-Objective Optimization
Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications.
Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues
Specifically, we firstly illuminate EDL from machine learning and EC and regard EDL as an optimization problem.
Alleviating Search Bias in Bayesian Evolutionary Optimization with Many Heterogeneous Objectives
To this end, we develop a multi-objective Bayesian evolutionary optimization approach to HE-MOPs by exploiting the different data sets on the cheap and expensive objectives in HE-MOPs to alleviate the search bias caused by the heterogeneous evaluation costs for evaluating different objectives.
A Secure Federated Data-Driven Evolutionary Multi-objective Optimization Algorithm
To address this issue, this paper proposes a secure federated data-driven evolutionary multi-objective optimization algorithm to protect both the raw data and the newly infilled solutions obtained by optimizing the acquisition function conducted on the server.
End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location
The network outputs are then converted into the probability distribution of the Pareto set, from which a set of non-dominated solutions can be sampled non-autoregressively.
Intelligent Computing: The Latest Advances, Challenges and Future
In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications.
A Graph Neural Network with Negative Message Passing for Graph Coloring
In this paper, we propose a graph network model for graph coloring, which is a class of representative heterophilous problems.
Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore
Besides, we provide a novel model GraphCore via VIIFs that can fast implement unsupervised FSAD training and can improve the performance of anomaly detection.
Augmented Transformers with Adaptive n-grams Embedding for Multilingual Scene Text Recognition
While vision transformers have been highly successful in improving the performance in image-based tasks, not much work has been reported on applying transformers to multilingual scene text recognition due to the complexities in the visual appearance of multilingual texts.
Evolutionary Reinforcement Learning: A Survey
This article presents a comprehensive survey of state-of-the-art methods for integrating EC into RL, referred to as evolutionary reinforcement learning (EvoRL).
What makes a good data augmentation for few-shot unsupervised image anomaly detection?
Data augmentation is a promising technique for unsupervised anomaly detection in industrial applications, where the availability of positive samples is often limited due to factors such as commercial competition and sample collection difficulties.
Lightweight Structure-aware Transformer Network for VHR Remote Sensing Image Change Detection
Popular Transformer networks have been successfully applied to remote sensing (RS) image change detection (CD) identifications and achieve better results than most convolutional neural networks (CNNs), but they still suffer from two main problems.
Identification of Energy Management Configuration Concepts from a Set of Pareto-optimal Solutions
In this study we focus on utilizing the concept identification technique for finding relevant and viable energy management configurations from a very large data set of Pareto-optimal solutions.
K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment
To further reflect the frequency-specific information from the magnetic resonance imaging principles, both k-space features and vision features are obtained and employed in our comprehensive encoders with a frequency reconstruction penalty.
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems
It has been shown that learning-based methods outperform traditional heuristics and mathematical solvers on the Traveling Salesman Problem (TSP) in terms of both performance and computational efficiency.
A Novel Dual-Stage Evolutionary Algorithm for Finding Robust Solutions
The primary objective of the peak-detection stage is to identify peaks in the fitness landscape of the original optimization problem.
Multiform Evolution for High-Dimensional Problems with Low Effective Dimensionality
In this paper, we scale evolutionary algorithms to high-dimensional optimization problems that deceptively possess a low effective dimensionality (certain dimensions do not significantly affect the objective function).
Reinforcement Learning for Scalable Train Timetable Rescheduling with Graph Representation
Train timetable rescheduling (TTR) aims to promptly restore the original operation of trains after unexpected disturbances or disruptions.
Knowledge-Assisted Dual-Stage Evolutionary Optimization of Large-Scale Crude Oil Scheduling
On the basis of the proposed model, a dual-stage evolutionary algorithm driven by heuristic rules (denoted by DSEA/HR) is developed, where the dual-stage search mechanism consists of global search and local refinement.
EmoDM: A Diffusion Model for Evolutionary Multi-objective Optimization
Evolutionary algorithms have been successful in solving multi-objective optimization problems (MOPs).
Constrained Multi-objective Optimization with Deep Reinforcement Learning Assisted Operator Selection
By using a Q-Network to learn a policy to estimate the Q-values of all actions, the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance.
Diffusion Model-Based Multiobjective Optimization for Gasoline Blending Scheduling
The presence of nonlinearity, integer constraints, and a large number of decision variables adds complexity to this problem, posing challenges for traditional and evolutionary algorithms.
A Composite Decomposition Method for Large-Scale Global Optimization
Furthermore, to enhance the efficiency and accuracy of CSG, we introduce two innovative methods: a multiplicatively separable variable detection method and a non-separable variable grouping method.
An Enhanced Differential Grouping Method for Large-Scale Overlapping Problems
In this article, we propose a two-stage enhanced grouping method for large-scale overlapping problems, called OEDG, which achieves accurate grouping while significantly reducing computational resource consumption.
