Michael W. Spratling

According to our database, Michael W. Spratling authored at least 60 papers between 1996 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Filter competition results in more robust Convolutional Neural Networks.

Bo Gao, Michael W. Spratling

Neurocomputing, 2025

2024

Learning multi-modal recurrent neural networks with target propagation.

Nikolay Manchev, Michael W. Spratling

Comput. Intell., August, 2024

When Multitask Learning Meets Partial Supervision: A Computer Vision Review.

Maxime Fontana, Michael W. Spratling, Miaojing Shi

Proc. IEEE, June, 2024

Query semantic reconstruction for background in few-shot segmentation.

Haoyan Guan, Michael W. Spratling

Vis. Comput., February, 2024

Optimizing Dense Visual Predictions Through Multi-Task Coherence and Prioritization.

Maxime Fontana, Michael W. Spratling, Miaojing Shi

CoRR, 2024

Few-Shot Anomaly Detection via Category-Agnostic Registration Learning.

Chaoqin Huang, Haoyan Guan, Aofan Jiang, Yanfeng Wang, Michael W. Spratling, Xinchao Wang, Ya Zhang

CoRR, 2024

OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift.

Lin Li, Yifei Wang, Chawin Sitawarin, Michael W. Spratling

Proceedings of the Forty-first International Conference on Machine Learning, 2024

One Prompt Word is Enough to Boost Adversarial Robustness for Pre-Trained Vision-Language Models.

Lin Li, Haoyan Guan, Jianing Qiu, Michael W. Spratling

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

Explaining away results in more robust visual tracking.

Bo Gao, Michael W. Spratling

Vis. Comput., May, 2023

Understanding and combating robust overfitting via input loss landscape analysis and regularization.

Lin Li, Michael W. Spratling

Pattern Recognit., April, 2023

OODRobustBench: benchmarking and analyzing adversarial robustness under distribution shift.

Lin Li, Yifei Wang, Chawin Sitawarin, Michael W. Spratling

CoRR, 2023

Comprehensive Assessment of the Performance of Deep Learning Classifiers Reveals a Surprising Lack of Robustness.

Michael W. Spratling

CoRR, 2023

When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review.

Maxime Fontana, Michael W. Spratling, Miaojing Shi

CoRR, 2023

AROID: Improving Adversarial Robustness through Online Instance-wise Data Augmentation.

Lin Li, Jianing Qiu, Michael W. Spratling

CoRR, 2023

Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing.

Lin Li, Michael W. Spratling

CoRR, 2023

Rethinking the Backbone Architecture for Tiny Object Detection.

Jinlai Ning, Haoyan Guan, Michael W. Spratling

Proceedings of the 18th International Joint Conference on Computer Vision, 2023

Data augmentation alone can improve adversarial training.

Lin Li, Michael W. Spratling

Proceedings of the Eleventh International Conference on Learning Representations, 2023

The Importance of Anti-Aliasing in Tiny Object Detection.

Jinlai Ning, Michael W. Spratling

Proceedings of the Asian Conference on Machine Learning, 2023

2022

More robust object tracking via shape and motion cue integration.

Bo Gao, Michael W. Spratling

Signal Process., 2022

Shape-Texture Debiased Training for Robust Template Matching.

Bo Gao, Michael W. Spratling

Sensors, 2022

On the biological plausibility of orthogonal initialisation for solving gradient instability in deep neural networks.

Nikolay Manchev, Michael W. Spratling

CoRR, 2022

CobNet: Cross Attention on Object and Background for Few-Shot Segmentation.

Haoyan Guan, Michael W. Spratling

Proceedings of the 26th International Conference on Pattern Recognition, 2022

Registration Based Few-Shot Anomaly Detection.

Chaoqin Huang, Haoyan Guan, Aofan Jiang, Ya Zhang, Michael W. Spratling, Yanfeng Wang

Proceedings of the Computer Vision, 2022

2020

Explaining away results in accurate and tolerant template matching.

Michael W. Spratling

Pattern Recognit., 2020

Target Propagation in Recurrent Neural Networks.

Nikolay Manchev, Michael W. Spratling

J. Mach. Learn. Res., 2020

Robust Template Matching via Hierarchical Convolutional Features from a Shape Biased CNN.

Bo Gao, Michael W. Spratling

CoRR, 2020

2018

Contour detection refined by a sparse reconstruction-based discrimination method.

Qi Wang, Michael W. Spratling

Signal Image Video Process., 2018

Two collaborative filtering recommender systems based on sparse dictionary coding.

Ismail Emre Kartoglu, Michael W. Spratling

Knowl. Inf. Syst., 2018

2017

A Neural Model of Coordinated Head and Eye Movement Control.

Muhammad Wasif, Michael W. Spratling

J. Intell. Robotic Syst., 2017

A Hierarchical Predictive Coding Model of Object Recognition in Natural Images.

Michael W. Spratling

Cogn. Comput., 2017

A neural model for eye-head-arm coordination.

Muhammad Wasif, Michael W. Spratling

Adv. Robotics, 2017

An Integrated System based on Binocular Learned Receptive Fields for Saccade-vergence on Visually Salient Targets.

Daniele Re, Agostino Gibaldi, Silvio P. Sabatini, Michael W. Spratling

Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 6: VISAPP, Porto, Portugal, February 27, 2017

2016

A simplified texture gradient method for improved image segmentation.

Qi Wang, Michael W. Spratling

Signal Image Video Process., 2016

A neural implementation of the Hough transform and the advantages of explaining away.

Michael W. Spratling

Image Vis. Comput., 2016

Predictive coding as a model of cognition.

Michael W. Spratling

Cogn. Process., 2016

A three-dimensional approach to Visual Speech Recognition using Discrete Cosine Transforms.

Toni Heidenreich, Michael W. Spratling

CoRR, 2016

A neural implementation of Bayesian inference based on predictive coding.

Michael W. Spratling

Connect. Sci., 2016

Contour Detection in Colour Images Using a Neurophysiologically Inspired Model.

Qi Wang, Michael W. Spratling

Cogn. Comput., 2016

2015

A neural model of binocular saccade planning and vergence control.

Muhammad Wasif, Michael W. Spratling

Adapt. Behav., 2015

2014

Predictive Coding.

Michael W. Spratling

Proceedings of the Encyclopedia of Computational Neuroscience, 2014

A single functional model of drivers and modulators in cortex.

Michael W. Spratling

J. Comput. Neurosci., 2014

Classification using sparse representations: a biologically plausible approach.

Michael W. Spratling

Biol. Cybern., 2014

2013

Image Segmentation Using a Sparse Coding Model of Cortical Area V1.

Michael W. Spratling

IEEE Trans. Image Process., 2013

2012

Predictive coding as a model of the V1 saliency map hypothesis.

Michael W. Spratling

Neural Networks, 2012

Unsupervised Learning of Generative and Discriminative Weights Encoding Elementary Image Components in a Predictive Coding Model of Cortical Function.

Michael W. Spratling

Neural Comput., 2012

Predictive coding accounts for V1 response properties recorded using reverse correlation.

Michael W. Spratling

Biol. Cybern., 2012

2011

Multiplicative Gain Modulation Arises Through Unsupervised Learning in a Predictive Coding Model of Cortical Function.

Kris De Meyer, Michael W. Spratling

Neural Comput., 2011

2009

Learning Posture Invariant Spatial Representations Through Temporal Correlations.

Michael W. Spratling

IEEE Trans. Auton. Ment. Dev., 2009

Unsupervised Learning of Overlapping Image Components Using Divisive Input Modulation.

Michael W. Spratling, Kris De Meyer, Raul Kompass

Comput. Intell. Neurosci., 2009

2008

Reconciling predictive coding and biased competition models of cortical function.

Michael W. Spratling

Frontiers Comput. Neurosci., 2008

2006

Learning Image Components for Object Recognition.

Michael W. Spratling

J. Mach. Learn. Res., 2006

2005

Learning Viewpoint Invariant Perceptual Representations from Cluttered Images.

Michael W. Spratling

IEEE Trans. Pattern Anal. Mach. Intell., 2005

2004

Neural coding strategies and mechanisms of competition.

Michael W. Spratling, M. H. Johnson

Cogn. Syst. Res., 2004

2003

Exploring the functional significance of dendritic inhibition in cortical pyramidal cells.

Michael W. Spratling, M. H. Johnson

Neurocomputing, 2003

2002

Preintegration Lateral Inhibition Enhances Unsupervised Learning.

Michael W. Spratling, M. H. Johnson

Neural Comput., 2002

2000

Learning Synaptic Clusters for Nonlinear Dendritic Processing.

Michael W. Spratling, Gillian M. Hayes

Neural Process. Lett., 2000

1999

Artificial ontogenesis : a connectionist model of development.

Michael W. Spratling

PhD thesis, 1999

1998

Learning sensory-motor cortical mappings without training.

Michael W. Spratling, Gillian Hayes

Proceedings of the 6th European Symposium on Artificial Neural Networks, 1998

A self-organising neural network for modelling cortical development.

Michael W. Spratling, Gillian Hayes

Proceedings of the 6th European Symposium on Artificial Neural Networks, 1998

1996

Uncalibrated Visual Servoing.

Michael W. Spratling, Roberto Cipolla

Proceedings of the British Machine Vision Conference 1996, 1996