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Michael W. Spratling
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2020 – today
- 2024
- [j36]Nikolay Manchev
, Michael W. Spratling:
Learning multi-modal recurrent neural networks with target propagation. Comput. Intell. 40(4) (2024) - [j35]Maxime Fontana
When Multitask Learning Meets Partial Supervision: A Computer Vision Review. Proc. IEEE 112(6): 516-543 (2024) - [j34]Haoyan Guan
Query semantic reconstruction for background in few-shot segmentation. Vis. Comput. 40(2): 799-810 (2024) - [c11]Lin Li, Haoyan Guan, Jianing Qiu, Michael W. Spratling:
One Prompt Word is Enough to Boost Adversarial Robustness for Pre-Trained Vision-Language Models. CVPR 2024: 24408-24419 - [c10]Lin Li, Yifei Wang, Chawin Sitawarin, Michael W. Spratling:
OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift. ICML 2024 - [i18]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 abs/2403.01849 (2024) - [i17]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 abs/2406.08810 (2024) - 2023
- [j33]Lin Li
Understanding and combating robust overfitting via input loss landscape analysis and regularization. Pattern Recognit. 136: 109229 (2023) - [j32]Bo Gao
Explaining away results in more robust visual tracking. Vis. Comput. 39(5): 2081-2095 (2023) - [c9]Jinlai Ning, Michael W. Spratling:
The Importance of Anti-Aliasing in Tiny Object Detection. ACML 2023: 975-990 - [c8]Lin Li, Michael W. Spratling:
Data augmentation alone can improve adversarial training. ICLR 2023 - [c7]Jinlai Ning, Haoyan Guan, Michael W. Spratling:
Rethinking the Backbone Architecture for Tiny Object Detection. VISIGRAPP (5: VISAPP) 2023: 103-114 - [i16]Lin Li, Michael W. Spratling
Data Augmentation Alone Can Improve Adversarial Training. CoRR abs/2301.09879 (2023) - [i15]Jinlai Ning, Haoyan Guan, Michael W. Spratling
Rethinking the backbone architecture for tiny object detection. CoRR abs/2303.11267 (2023) - [i14]Lin Li, Michael W. Spratling
Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing. CoRR abs/2303.14077 (2023) - [i13]Lin Li, Jianing Qiu, Michael W. Spratling:
AROID: Improving Adversarial Robustness through Online Instance-wise Data Augmentation. CoRR abs/2306.07197 (2023) - [i12]Maxime Fontana, Michael W. Spratling, Miaojing Shi:
When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review. CoRR abs/2307.14382 (2023) - [i11]Michael W. Spratling
Comprehensive Assessment of the Performance of Deep Learning Classifiers Reveals a Surprising Lack of Robustness. CoRR abs/2308.04137 (2023) - [i10]Lin Li, Yifei Wang, Chawin Sitawarin
OODRobustBench: benchmarking and analyzing adversarial robustness under distribution shift. CoRR abs/2310.12793 (2023) - [i9]Jinlai Ning, Michael W. Spratling
The Importance of Anti-Aliasing in Tiny Object Detection. CoRR abs/2310.14221 (2023) - 2022
- [j31]Bo Gao
Shape-Texture Debiased Training for Robust Template Matching. Sensors 22(17): 6658 (2022) - [j30]Bo Gao
More robust object tracking via shape and motion cue integration. Signal Process. 199: 108628 (2022) - [c6]Chaoqin Huang
Registration Based Few-Shot Anomaly Detection. ECCV (24) 2022: 303-319 - [c5]Haoyan Guan, Michael W. Spratling:
CobNet: Cross Attention on Object and Background for Few-Shot Segmentation. ICPR 2022: 39-45 - [i8]Chaoqin Huang, Haoyan Guan, Aofan Jiang, Ya Zhang
Registration based Few-Shot Anomaly Detection. CoRR abs/2207.07361 (2022) - [i7]Haoyan Guan, Michael W. Spratling
CobNet: Cross Attention on Object and Background for Few-Shot Segmentation. CoRR abs/2210.11968 (2022) - [i6]Haoyan Guan, Michael W. Spratling
Query Semantic Reconstruction for Background in Few-Shot Segmentation. CoRR abs/2210.12055 (2022) - [i5]Nikolay Manchev
On the biological plausibility of orthogonal initialisation for solving gradient instability in deep neural networks. CoRR abs/2211.08408 (2022) - [i4]Lin Li, Michael W. Spratling
Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and Regularization. CoRR abs/2212.04985 (2022) - 2020
- [j29]Nikolay Manchev, Michael W. Spratling
Target Propagation in Recurrent Neural Networks. J. Mach. Learn. Res. 21: 7:1-7:33 (2020) - [j28]Michael W. Spratling
Explaining away results in accurate and tolerant template matching. Pattern Recognit. 104: 107337 (2020) - [i3]Bo Gao, Michael W. Spratling:
Robust Template Matching via Hierarchical Convolutional Features from a Shape Biased CNN. CoRR abs/2007.15817 (2020)
2010 – 2019
- 2019
- [i2]Michael W. Spratling:
Explaining Away Results in Accurate and Tolerant Template Matching. CoRR abs/1911.04169 (2019) - 2018
- [j27]Ismail Emre Kartoglu, Michael W. Spratling
Two collaborative filtering recommender systems based on sparse dictionary coding. Knowl. Inf. Syst. 57(3): 709-720 (2018) - [j26]Qi Wang, Michael W. Spratling
Contour detection refined by a sparse reconstruction-based discrimination method. Signal Image Video Process. 12(2): 207-214 (2018) - 2017
- [j25]Muhammad Wasif
A neural model for eye-head-arm coordination. Adv. Robotics 31(12): 650-663 (2017) - [j24]Michael W. Spratling
A Hierarchical Predictive Coding Model of Object Recognition in Natural Images. Cogn. Comput. 9(2): 151-167 (2017) - [j23]Muhammad Wasif
A Neural Model of Coordinated Head and Eye Movement Control. J. Intell. Robotic Syst. 85(1): 107-126 (2017) - [c4]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: 204-215 - 2016
- [j22]Qi Wang, Michael W. Spratling
Contour Detection in Colour Images Using a Neurophysiologically Inspired Model. Cogn. Comput. 8(6): 1027-1035 (2016) - [j21]Michael W. Spratling
A neural implementation of Bayesian inference based on predictive coding. Connect. Sci. 28(4): 346-383 (2016) - [j20]Michael W. Spratling
Predictive coding as a model of cognition. Cogn. Process. 17(3): 279-305 (2016) - [j19]Michael W. Spratling
A neural implementation of the Hough transform and the advantages of explaining away. Image Vis. Comput. 52: 15-24 (2016) - [j18]Qi Wang, Michael W. Spratling
A simplified texture gradient method for improved image segmentation. Signal Image Video Process. 10(4): 679-686 (2016) - [i1]Toni Heidenreich, Michael W. Spratling:
A three-dimensional approach to Visual Speech Recognition using Discrete Cosine Transforms. CoRR abs/1609.01932 (2016) - 2015
- [j17]Muhammad Wasif
A neural model of binocular saccade planning and vergence control. Adapt. Behav. 23(5): 265-282 (2015) - 2014
- [j16]Michael W. Spratling
Classification using sparse representations: a biologically plausible approach. Biol. Cybern. 108(1): 61-73 (2014) - [j15]Michael W. Spratling
A single functional model of drivers and modulators in cortex. J. Comput. Neurosci. 36(1): 97-118 (2014) - [r1]Michael W. Spratling:
Predictive Coding. Encyclopedia of Computational Neuroscience 2014 - 2013
- [j14]Michael W. Spratling
Image Segmentation Using a Sparse Coding Model of Cortical Area V1. IEEE Trans. Image Process. 22(4): 1631-1643 (2013) - 2012
- [j13]Michael W. Spratling
Predictive coding accounts for V1 response properties recorded using reverse correlation. Biol. Cybern. 106(1): 37-49 (2012) - [j12]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): 60-103 (2012) - [j11]Michael W. Spratling
Predictive coding as a model of the V1 saliency map hypothesis. Neural Networks 26: 7-28 (2012) - 2011
- [j10]Kris De Meyer
Multiplicative Gain Modulation Arises Through Unsupervised Learning in a Predictive Coding Model of Cortical Function. Neural Comput. 23(6): 1536-1567 (2011)
2000 – 2009
- 2009
- [j9]Michael W. Spratling
Unsupervised Learning of Overlapping Image Components Using Divisive Input Modulation. Comput. Intell. Neurosci. 2009: 381457:1-381457:19 (2009) - [j8]Michael W. Spratling
Learning Posture Invariant Spatial Representations Through Temporal Correlations. IEEE Trans. Auton. Ment. Dev. 1(4): 253-263 (2009) - 2008
- [j7]Michael W. Spratling
Reconciling predictive coding and biased competition models of cortical function. Frontiers Comput. Neurosci. 2: 4 (2008) - 2006
- [j6]Michael W. Spratling:
Learning Image Components for Object Recognition. J. Mach. Learn. Res. 7: 793-815 (2006) - 2005
- [j5]Michael W. Spratling
Learning Viewpoint Invariant Perceptual Representations from Cluttered Images. IEEE Trans. Pattern Anal. Mach. Intell. 27(5): 753-761 (2005) - 2004
- [j4]Michael W. Spratling
Neural coding strategies and mechanisms of competition. Cogn. Syst. Res. 5(2): 93-117 (2004) - 2003
- [j3]Michael W. Spratling
Exploring the functional significance of dendritic inhibition in cortical pyramidal cells. Neurocomputing 52-54: 389-395 (2003) - 2002
- [j2]Michael W. Spratling
Preintegration Lateral Inhibition Enhances Unsupervised Learning. Neural Comput. 14(9): 2157-2179 (2002) - 2000
- [j1]Michael W. Spratling
Learning Synaptic Clusters for Nonlinear Dendritic Processing. Neural Process. Lett. 11(1): 17-27 (2000)
1990 – 1999
- 1999
- [b1]Michael W. Spratling:
Artificial ontogenesis : a connectionist model of development. University of Edinburgh, UK, 1999 - 1998
- [c3]Michael W. Spratling, Gillian Hayes:
A self-organising neural network for modelling cortical development. ESANN 1998: 333-338 - [c2]Michael W. Spratling, Gillian Hayes:
Learning sensory-motor cortical mappings without training. ESANN 1998: 339-344 - 1996
- [c1]Michael W. Spratling, Roberto Cipolla:
Uncalibrated Visual Servoing. BMVC 1996: 1-10
Coauthor Index
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last updated on 2024-11-15 19:33 CET by the dblp team
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