1.1k citations · 2.1k across the 32 of their papers we have counts for
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Proximal Mean-field for Neural Network Quantization
Thalaiyasingam Ajanthan, Puneet K. Dokania, Richard Hartley +1
Compressing large Neural Networks (NN) by quantizing the parameters, while maintaining the performance is highly desirable due to reduced memory and time complexity. In this work,…
Generalized Range Moves
Richard Hartley, Thalaiyasingam Ajanthan
We consider move-making algorithms for energy minimization of multi-label Markov Random Fields (MRFs). Since this is not a tractable problem in general, a commonly used heuristic i…
SNIP: Single-shot Network Pruning based on Connection Sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, Philip H. S. Torr
Pruning large neural networks while maintaining their performance is often desirable due to the reduced space and time complexity. In existing methods, pruning is done within an it…
Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials
Thomas Joy, Alban Desmaison, Thalaiyasingam Ajanthan +5
Dense conditional random fields (CRFs) have become a popular framework for modelling several problems in computer vision such as stereo correspondence and multi-class semantic segm…
DGPose: Deep Generative Models for Human Body Analysis
Rodrigo de Bem, Arnab Ghosh, Thalaiyasingam Ajanthan +4
Deep generative modelling for human body analysis is an emerging problem with many interesting applications. However, the latent space learned by such approaches is typically not i…
Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence
Arslan Chaudhry, Puneet K. Dokania, Thalaiyasingam Ajanthan +1
Incremental learning (IL) has received a lot of attention recently, however, the literature lacks a precise problem definition, proper evaluation settings, and metrics tailored spe…