Michael W. Spratling

ORCID

Publication Activity (10 Years)

Years Active: 1996-2024
Publications (10 Years): 43

Top Venues

CoRR

Pattern Recognit.

Vis. Comput.

Signal Image Video Process.

University of Edinburgh, UK (PHD)

Education History

University of Edinburgh, UK

PHD

Publications

Haoyan Guan, Michael W. Spratling
Query semantic reconstruction for background in few-shot segmentation. Vis. Comput. 40 (2) (2024)
Chaoqin Huang, Haoyan Guan, Aofan Jiang, Yanfeng Wang, Michael W. Spratling, Xinchao Wang, Ya Zhang
Few-Shot Anomaly Detection via Category-Agnostic Registration Learning. CoRR (2024)
Nikolay Manchev, Michael W. Spratling
Learning multi-modal recurrent neural networks with target propagation. Comput. Intell. 40 (4) (2024)
Lin Li, Haoyan Guan, Jianing Qiu, Michael W. Spratling
One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models. CoRR (2024)
Jinlai Ning, Michael W. Spratling
The Importance of Anti-Aliasing in Tiny Object Detection. ACML (2023)
Lin Li, Michael W. Spratling
Data augmentation alone can improve adversarial training. ICLR (2023)
Lin Li, Yifei Wang, Chawin Sitawarin, Michael W. Spratling
OODRobustBench: benchmarking and analyzing adversarial robustness under distribution shift. CoRR (2023)
Lin Li, Michael W. Spratling
Understanding and combating robust overfitting via input loss landscape analysis and regularization. Pattern Recognit. 136 (2023)
Michael W. Spratling
Comprehensive Assessment of the Performance of Deep Learning Classifiers Reveals a Surprising Lack of Robustness. CoRR (2023)
Maxime Fontana, Michael W. Spratling, Miaojing Shi
When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review. CoRR (2023)
Lin Li, Michael W. Spratling
Data Augmentation Alone Can Improve Adversarial Training. CoRR (2023)
Lin Li, Jianing Qiu, Michael W. Spratling
AROID: Improving Adversarial Robustness through Online Instance-wise Data Augmentation. CoRR (2023)
Jinlai Ning, Michael W. Spratling
The Importance of Anti-Aliasing in Tiny Object Detection. CoRR (2023)
Lin Li, Michael W. Spratling
Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing. CoRR (2023)
Jinlai Ning, Haoyan Guan, Michael W. Spratling
Rethinking the Backbone Architecture for Tiny Object Detection. VISIGRAPP (5: VISAPP) (2023)
Bo Gao, Michael W. Spratling
Explaining away results in more robust visual tracking. Vis. Comput. 39 (5) (2023)
Jinlai Ning, Haoyan Guan, Michael W. Spratling
Rethinking the backbone architecture for tiny object detection. CoRR (2023)
Chaoqin Huang, Haoyan Guan, Aofan Jiang, Ya Zhang, Michael W. Spratling, Yan-Feng Wang
Registration Based Few-Shot Anomaly Detection. ECCV (24) (2022)
Haoyan Guan, Michael W. Spratling
Query Semantic Reconstruction for Background in Few-Shot Segmentation. CoRR (2022)
Lin Li, Michael W. Spratling
Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and Regularization. CoRR (2022)
Bo Gao, Michael W. Spratling
Shape-Texture Debiased Training for Robust Template Matching. Sensors 22 (17) (2022)
Haoyan Guan, Michael W. Spratling
CobNet: Cross Attention on Object and Background for Few-Shot Segmentation. ICPR (2022)
Haoyan Guan, Michael W. Spratling
CobNet: Cross Attention on Object and Background for Few-Shot Segmentation. CoRR (2022)
Bo Gao, Michael W. Spratling
More robust object tracking via shape and motion cue integration. Signal Process. 199 (2022)
Chaoqin Huang, Haoyan Guan, Aofan Jiang, Ya Zhang, Michael W. Spratling, Yan-Feng Wang
Registration based Few-Shot Anomaly Detection. CoRR (2022)
Nikolay Manchev, Michael W. Spratling
On the biological plausibility of orthogonal initialisation for solving gradient instability in deep neural networks. CoRR (2022)
Bo Gao, Michael W. Spratling
Robust Template Matching via Hierarchical Convolutional Features from a Shape Biased CNN. CoRR (2020)
Michael W. Spratling
Explaining away results in accurate and tolerant template matching. Pattern Recognit. 104 (2020)
Nikolay Manchev, Michael W. Spratling
Target Propagation in Recurrent Neural Networks. J. Mach. Learn. Res. 21 (2020)
Michael W. Spratling
Explaining Away Results in Accurate and Tolerant Template Matching. CoRR (2019)
Qi Wang, Michael W. Spratling
Contour detection refined by a sparse reconstruction-based discrimination method. Signal Image Video Process. 12 (2) (2018)
Ismail Emre Kartoglu, Michael W. Spratling
Two collaborative filtering recommender systems based on sparse dictionary coding. Knowl. Inf. Syst. 57 (3) (2018)
Muhammad Wasif, Michael W. Spratling
A neural model for eye-head-arm coordination. Adv. Robotics 31 (12) (2017)
Michael W. Spratling
A Hierarchical Predictive Coding Model of Object Recognition in Natural Images. Cogn. Comput. 9 (2) (2017)
Daniele Re, Agostino Gibaldi, Silvio P. Sabatini, Michael W. Spratling
An Integrated System based on Binocular Learned Receptive Fields for Saccade-vergence on Visually Salient Targets. VISIGRAPP (6: VISAPP) (2017)
Muhammad Wasif, Michael W. Spratling
A Neural Model of Coordinated Head and Eye Movement Control. J. Intell. Robotic Syst. 85 (1) (2017)
Michael W. Spratling
A neural implementation of Bayesian inference based on predictive coding. Connect. Sci. 28 (4) (2016)
Qi Wang, Michael W. Spratling
Contour Detection in Colour Images Using a Neurophysiologically Inspired Model. Cogn. Comput. 8 (6) (2016)
Toni Heidenreich, Michael W. Spratling
A three-dimensional approach to Visual Speech Recognition using Discrete Cosine Transforms. CoRR (2016)
Qi Wang, Michael W. Spratling
A simplified texture gradient method for improved image segmentation. Signal Image Video Process. 10 (4) (2016)
Michael W. Spratling
Predictive coding as a model of cognition. Cogn. Process. 17 (3) (2016)
Michael W. Spratling
A neural implementation of the Hough transform and the advantages of explaining away. Image Vis. Comput. 52 (2016)
Muhammad Wasif, Michael W. Spratling
A neural model of binocular saccade planning and vergence control. Adapt. Behav. 23 (5) (2015)
Michael W. Spratling
Classification using sparse representations: a biologically plausible approach. Biol. Cybern. 108 (1) (2014)
Michael W. Spratling
A single functional model of drivers and modulators in cortex. J. Comput. Neurosci. 36 (1) (2014)
Michael W. Spratling
Image Segmentation Using a Sparse Coding Model of Cortical Area V1. IEEE Trans. Image Process. 22 (4) (2013)
Michael W. Spratling
Predictive coding accounts for V1 response properties recorded using reverse correlation. Biol. Cybern. 106 (1) (2012)
Michael W. Spratling
Unsupervised Learning of Generative and Discriminative Weights Encoding Elementary Image Components in a Predictive Coding Model of Cortical Function. Neural Comput. 24 (1) (2012)
Michael W. Spratling
Predictive coding as a model of the V1 saliency map hypothesis. Neural Networks 26 (2012)
Kris De Meyer, Michael W. Spratling
Multiplicative Gain Modulation Arises Through Unsupervised Learning in a Predictive Coding Model of Cortical Function. Neural Comput. 23 (6) (2011)
Michael W. Spratling
Learning Posture Invariant Spatial Representations Through Temporal Correlations. IEEE Trans. Auton. Ment. Dev. 1 (4) (2009)
Michael W. Spratling, Kris De Meyer, R. Kompass
Unsupervised Learning of Overlapping Image Components Using Divisive Input Modulation. Comput. Intell. Neurosci. 2009 (2009)
Michael W. Spratling
Reconciling predictive coding and biased competition models of cortical function. Frontiers Comput. Neurosci. 2 (2008)
Michael W. Spratling
Learning Image Components for Object Recognition. J. Mach. Learn. Res. 7 (2006)
Michael W. Spratling
Learning Viewpoint Invariant Perceptual Representations from Cluttered Images. IEEE Trans. Pattern Anal. Mach. Intell. 27 (5) (2005)
Michael W. Spratling, M. H. Johnson
Neural coding strategies and mechanisms of competition. Cogn. Syst. Res. 5 (2) (2004)
Michael W. Spratling, M. H. Johnson
Exploring the functional significance of dendritic inhibition in cortical pyramidal cells. Neurocomputing (2003)
Michael W. Spratling, M. H. Johnson
Preintegration Lateral Inhibition Enhances Unsupervised Learning. Neural Comput. 14 (9) (2002)
Michael W. Spratling, Gillian M. Hayes
Learning Synaptic Clusters for Nonlinear Dendritic Processing. Neural Process. Lett. 11 (1) (2000)
Michael W. Spratling, Gillian Hayes
A self-organising neural network for modelling cortical development. ESANN (1998)
Michael W. Spratling, Gillian Hayes
Learning sensory-motor cortical mappings without training. ESANN (1998)
Michael W. Spratling, Roberto Cipolla
Uncalibrated Visual Servoing. BMVC (1996)

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Publication Activity (10 Years)

Top Topics

Top Venues

Michael W. Spratling