Michael W. Spratling

78 papers A* 3A 2B 3C 1Journal 63Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	FAAR: Efficient Frequency-Aware Multi-Task Fine-Tuning via Automatic Rank Selection. CoRR Maxime Fontana, Michael W. Spratling, Miaojing Shi
2025	J	`jnl`	A comprehensive assessment benchmark for rigorously evaluating deep learning image classifiers. Neural Networks Michael W. Spratling
2025	J	`jnl`	A margin-based replacement for cross-entropy loss. CoRR Michael W. Spratling, Heiko H. Schütt
2025	J	`jnl`	AROID: Improving Adversarial Robustness Through Online Instance-Wise Data Augmentation. Int. J. Comput. Vis. Lin Li, Jianing Qiu, Michael W. Spratling
2025	J	`jnl`	Few-Shot Anomaly Detection via Category-Agnostic Registration Learning. IEEE Trans. Neural Networks Learn. Syst. Chaoqin Huang, Haoyan Guan, Aofan Jiang, Ya Zhang, Michael W. Spratling, Xinchao Wang, Yanfeng Wang
2025	J	`jnl`	Filter competition results in more robust Convolutional Neural Networks. Neurocomputing Bo Gao, Michael W. Spratling
2025	A	`conf`	Optimizing Dense Visual Predictions Through Multi-Task Coherence and Prioritization. WACV Maxime Fontana, Michael W. Spratling, Miaojing Shi
2025	J	`jnl`	Robust shortcut and disordered robustness: Improving adversarial training through adaptive smoothing. Pattern Recognit. Lin Li, Michael W. Spratling
2025	J	`jnl`	Softplus Attention with Re-weighting Boosts Length Extrapolation in Large Language Models. CoRR Bo Gao, Michael W. Spratling
2024	J	`jnl`	Few-Shot Anomaly Detection via Category-Agnostic Registration Learning. CoRR Chaoqin Huang, Haoyan Guan, Aofan Jiang, Yanfeng Wang, Michael W. Spratling, Xinchao Wang, Ya Zhang
2024	J	`jnl`	Learning multi-modal recurrent neural networks with target propagation. Comput. Intell. Nikolay Manchev, Michael W. Spratling
2024	A*	`conf`	OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift. ICML Lin Li, Yifei Wang, Chawin Sitawarin, Michael W. Spratling
2024	A*	`conf`	One Prompt Word is Enough to Boost Adversarial Robustness for Pre-Trained Vision-Language Models. CVPR Lin Li, Haoyan Guan, Jianing Qiu, Michael W. Spratling
2024	J	`jnl`	One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models. CoRR Lin Li, Haoyan Guan, Jianing Qiu, Michael W. Spratling
2024	J	`jnl`	Optimizing Dense Visual Predictions Through Multi-Task Coherence and Prioritization. CoRR Maxime Fontana, Michael W. Spratling, Miaojing Shi
2024	J	`jnl`	Query semantic reconstruction for background in few-shot segmentation. Vis. Comput. Haoyan Guan, Michael W. Spratling
2024	J	`jnl`	When Multitask Learning Meets Partial Supervision: A Computer Vision Review. Proc. IEEE Maxime Fontana, Michael W. Spratling, Miaojing Shi
2023	J	`jnl`	AROID: Improving Adversarial Robustness through Online Instance-wise Data Augmentation. CoRR Lin Li, Jianing Qiu, Michael W. Spratling
2023	J	`jnl`	Comprehensive Assessment of the Performance of Deep Learning Classifiers Reveals a Surprising Lack of Robustness. CoRR Michael W. Spratling
2023	J	`jnl`	Data Augmentation Alone Can Improve Adversarial Training. CoRR Lin Li, Michael W. Spratling
2023	A*	`conf`	Data augmentation alone can improve adversarial training. ICLR Lin Li, Michael W. Spratling
2023	J	`jnl`	Explaining away results in more robust visual tracking. Vis. Comput. Bo Gao, Michael W. Spratling
2023	J	`jnl`	Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing. CoRR Lin Li, Michael W. Spratling
2023	J	`jnl`	OODRobustBench: benchmarking and analyzing adversarial robustness under distribution shift. CoRR Lin Li, Yifei Wang, Chawin Sitawarin, Michael W. Spratling
2023	—	`conf`	Rethinking the Backbone Architecture for Tiny Object Detection. VISIGRAPP (5: VISAPP) Jinlai Ning, Haoyan Guan, Michael W. Spratling
2023	J	`jnl`	Rethinking the backbone architecture for tiny object detection. CoRR Jinlai Ning, Haoyan Guan, Michael W. Spratling
2023	C	`conf`	The Importance of Anti-Aliasing in Tiny Object Detection. ACML Jinlai Ning, Michael W. Spratling
2023	J	`jnl`	The Importance of Anti-Aliasing in Tiny Object Detection. CoRR Jinlai Ning, Michael W. Spratling
2023	J	`jnl`	Understanding and combating robust overfitting via input loss landscape analysis and regularization. Pattern Recognit. Lin Li, Michael W. Spratling
2023	J	`jnl`	When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review. CoRR Maxime Fontana, Michael W. Spratling, Miaojing Shi
2022	B	`conf`	CobNet: Cross Attention on Object and Background for Few-Shot Segmentation. ICPR Haoyan Guan, Michael W. Spratling
2022	J	`jnl`	CobNet: Cross Attention on Object and Background for Few-Shot Segmentation. CoRR Haoyan Guan, Michael W. Spratling
2022	J	`jnl`	More robust object tracking via shape and motion cue integration. Signal Process. Bo Gao, Michael W. Spratling
2022	—	`conf`	On the Biological Plausibility of Orthogonal Initialisation for Solving Gradient Instability in Deep Neural Networks. ISCMI Nikolay Manchev, Michael W. Spratling
2022	J	`jnl`	On the biological plausibility of orthogonal initialisation for solving gradient instability in deep neural networks. CoRR Nikolay Manchev, Michael W. Spratling
2022	J	`jnl`	Query Semantic Reconstruction for Background in Few-Shot Segmentation. CoRR Haoyan Guan, Michael W. Spratling
2022	—	`conf`	Registration Based Few-Shot Anomaly Detection. ECCV (24) Chaoqin Huang, Haoyan Guan, Aofan Jiang, Ya Zhang, Michael W. Spratling, Yanfeng Wang
2022	J	`jnl`	Registration based Few-Shot Anomaly Detection. CoRR Chaoqin Huang, Haoyan Guan, Aofan Jiang, Ya Zhang, Michael W. Spratling, Yanfeng Wang
2022	J	`jnl`	Shape-Texture Debiased Training for Robust Template Matching. Sensors Bo Gao, Michael W. Spratling
2022	J	`jnl`	Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and Regularization. CoRR Lin Li, Michael W. Spratling
2020	J	`jnl`	Explaining away results in accurate and tolerant template matching. Pattern Recognit. Michael W. Spratling
2020	J	`jnl`	Robust Template Matching via Hierarchical Convolutional Features from a Shape Biased CNN. CoRR Bo Gao, Michael W. Spratling
2020	J	`jnl`	Target Propagation in Recurrent Neural Networks. J. Mach. Learn. Res. Nikolay Manchev, Michael W. Spratling
2019	J	`jnl`	Explaining Away Results in Accurate and Tolerant Template Matching. CoRR Michael W. Spratling
2018	J	`jnl`	Contour detection refined by a sparse reconstruction-based discrimination method. Signal Image Video Process. Qi Wang, Michael W. Spratling
2018	J	`jnl`	Two collaborative filtering recommender systems based on sparse dictionary coding. Knowl. Inf. Syst. Ismail Emre Kartoglu, Michael W. Spratling
2017	J	`jnl`	A Hierarchical Predictive Coding Model of Object Recognition in Natural Images. Cogn. Comput. Michael W. Spratling
2017	J	`jnl`	A Neural Model of Coordinated Head and Eye Movement Control. J. Intell. Robotic Syst. Muhammad Wasif, Michael W. Spratling
2017	J	`jnl`	A neural model for eye-head-arm coordination. Adv. Robotics Muhammad Wasif, Michael W. Spratling
2017	—	`conf`	An Integrated System based on Binocular Learned Receptive Fields for Saccade-vergence on Visually Salient Targets. VISIGRAPP (6: VISAPP) Daniele Re, Agostino Gibaldi, Silvio P. Sabatini, Michael W. Spratling
2016	J	`jnl`	A neural implementation of Bayesian inference based on predictive coding. Connect. Sci. Michael W. Spratling
2016	J	`jnl`	A neural implementation of the Hough transform and the advantages of explaining away. Image Vis. Comput. Michael W. Spratling
2016	J	`jnl`	A simplified texture gradient method for improved image segmentation. Signal Image Video Process. Qi Wang, Michael W. Spratling
2016	J	`jnl`	A three-dimensional approach to Visual Speech Recognition using Discrete Cosine Transforms. CoRR Toni Heidenreich, Michael W. Spratling
2016	J	`jnl`	Contour Detection in Colour Images Using a Neurophysiologically Inspired Model. Cogn. Comput. Qi Wang, Michael W. Spratling
2016	J	`jnl`	Predictive coding as a model of cognition. Cogn. Process. Michael W. Spratling
2015	J	`jnl`	A neural model of binocular saccade planning and vergence control. Adapt. Behav. Muhammad Wasif, Michael W. Spratling
2014	J	`jnl`	A single functional model of drivers and modulators in cortex. J. Comput. Neurosci. Michael W. Spratling
2014	J	`jnl`	Classification using sparse representations: a biologically plausible approach. Biol. Cybern. Michael W. Spratling
2014	—	`ch.`	Predictive Coding. Encyclopedia of Computational Neuroscience Michael W. Spratling
2013	J	`jnl`	Image Segmentation Using a Sparse Coding Model of Cortical Area V1. IEEE Trans. Image Process. Michael W. Spratling
2012	J	`jnl`	Predictive coding accounts for V1 response properties recorded using reverse correlation. Biol. Cybern. Michael W. Spratling
2012	J	`jnl`	Predictive coding as a model of the V1 saliency map hypothesis. Neural Networks Michael W. Spratling
2012	J	`jnl`	Unsupervised Learning of Generative and Discriminative Weights Encoding Elementary Image Components in a Predictive Coding Model of Cortical Function. Neural Comput. Michael W. Spratling
2011	J	`jnl`	Multiplicative Gain Modulation Arises Through Unsupervised Learning in a Predictive Coding Model of Cortical Function. Neural Comput. Kris De Meyer, Michael W. Spratling
2009	J	`jnl`	Learning Posture Invariant Spatial Representations Through Temporal Correlations. IEEE Trans. Auton. Ment. Dev. Michael W. Spratling
2009	J	`jnl`	Unsupervised Learning of Overlapping Image Components Using Divisive Input Modulation. Comput. Intell. Neurosci. Michael W. Spratling, Kris De Meyer, Raul Kompass
2008	J	`jnl`	Reconciling predictive coding and biased competition models of cortical function. Frontiers Comput. Neurosci. Michael W. Spratling
2006	J	`jnl`	Learning Image Components for Object Recognition. J. Mach. Learn. Res. Michael W. Spratling
2005	J	`jnl`	Learning Viewpoint Invariant Perceptual Representations from Cluttered Images. IEEE Trans. Pattern Anal. Mach. Intell. Michael W. Spratling
2004	J	`jnl`	Neural coding strategies and mechanisms of competition. Cogn. Syst. Res. Michael W. Spratling, M. H. Johnson
2003	J	`jnl`	Exploring the functional significance of dendritic inhibition in cortical pyramidal cells. Neurocomputing Michael W. Spratling, M. H. Johnson
2002	J	`jnl`	Preintegration Lateral Inhibition Enhances Unsupervised Learning. Neural Comput. Michael W. Spratling, M. H. Johnson
2000	J	`jnl`	Learning Synaptic Clusters for Nonlinear Dendritic Processing. Neural Process. Lett. Michael W. Spratling, Gillian M. Hayes
1999	—	`—`	Artificial ontogenesis : a connectionist model of development. Michael W. Spratling
1998	B	`conf`	A self-organising neural network for modelling cortical development. ESANN Michael W. Spratling, Gillian Hayes
1998	B	`conf`	Learning sensory-motor cortical mappings without training. ESANN Michael W. Spratling, Gillian Hayes
1996	A	`conf`	Uncalibrated Visual Servoing. BMVC Michael W. Spratling, Roberto Cipolla