Thalaiyasingam Ajanthan
Thalaiyasingam Ajanthan
Потвърден имейл адрес: anu.edu.au - Начална страница
Snip: Single-shot network pruning based on connection sensitivity
N Lee, T Ajanthan, PHS Torr
arXiv preprint arXiv:1810.02340, 2018
Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence
A Chaudhry, PK Dokania, T Ajanthan, PHS Torr
European Conference on Computer Vision (ECCV), 2018
Continual learning with tiny episodic memories
A Chaudhry, M Rohrbach, M Elhoseiny, T Ajanthan, P Dokania, P Torr, ...
Workshop on Multi-Task and Lifelong Reinforcement Learning, 2019
A signal propagation perspective for pruning neural networks at initialization
N Lee, T Ajanthan, S Gould, PHS Torr
arXiv preprint arXiv:1906.06307, 2019
Calibration of neural networks using splines
K Gupta, A Rahimi, T Ajanthan, T Mensink, C Sminchisescu, R Hartley
arXiv preprint arXiv:2006.12800, 2020
Learning to adapt for stereo
A Tonioni, O Rahnama, T Joy, LD Stefano, T Ajanthan, PHS Torr
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
Retrieval augmented classification for long-tail visual recognition
A Long, W Yin, T Ajanthan, V Nguyen, P Purkait, R Garg, A Blair, C Shen, ...
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022
Mirror descent view for neural network quantization
T Ajanthan, K Gupta, P Torr, R Hartley, P Dokania
International conference on artificial intelligence and statistics, 2809-2817, 2021
A conditional deep generative model of people in natural images
R De Bem, A Ghosh, A Boukhayma, T Ajanthan, N Siddharth, P Torr
2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 1449-1458, 2019
Efficient Linear Programming for Dense CRFs
T Ajanthan, A Desmaison, R Bunel, M Salzmann, PHS Torr, MP Kumar
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
Understanding and improving the role of projection head in self-supervised learning
K Gupta, T Ajanthan, A Hengel, S Gould
arXiv preprint arXiv:2212.11491, 2022
Improved gradient-based adversarial attacks for quantized networks
K Gupta, T Ajanthan
Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 6810-6818, 2022
Proximal mean-field for neural network quantization
T Ajanthan, PK Dokania, R Hartley, PHS Torr
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
Bidirectionally self-normalizing neural networks
Y Lu, S Gould, T Ajanthan
Neural Networks 167, 283-291, 2023
Automatic Number Plate Recognition in Low Quality Videos
T Ajanthan, P Kamalaruban, R Rodrigo
Industrial and Information Systems (ICIIS), 566 - 571, 2013
A Semi-supervised Deep Generative Model for Human Body Analysis
R de Bem, A Ghosh, T Ajanthan, O Miksik, N Siddharth, P Torr
ECCV Workshop on Human Behaviour Understanding, 2018
Pairwise similarity knowledge transfer for weakly supervised object localization
A Rahimi, A Shaban, T Ajanthan, R Hartley, B Boots
European conference on computer vision, 395-412, 2020
Dgpose: Deep generative models for human body analysis
R de Bem, A Ghosh, T Ajanthan, O Miksik, A Boukhayma, N Siddharth, ...
International Journal of Computer Vision 128, 1537-1563, 2020
Iteratively Reweighted Graph Cut for Multi-Label MRFs With Non-Convex Priors
T Ajanthan, R Hartley, M Salzmann, H Li
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5144-5152, 2015
Post-hoc calibration of neural networks
A Rahimi, K Gupta, T Ajanthan, T Mensink, C Sminchisescu, R Hartley
arXiv preprint arXiv:2006.12807 2, 2020
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