5 citations · 11 across the 9 of their papers we have counts for
10 papers
Probabilistic and Semantic Descriptions of Image Manifolds and Their Applications
Peter Tu, Zhaoyuan Yang, Richard Hartley +6
This paper begins with a description of methods for estimating image probability density functions that reflects the observation that such data is usually constrained to lie in res…
PMaF: Deep Declarative Layers for Principal Matrix Features
Zhiwei Xu, Hao Wang, Yanbin Liu +1
We explore two differentiable deep declarative layers, namely least squares on sphere (LESS) and implicit eigen decomposition (IED), for learning the principal matrix features (PMa…
Towards Understanding Gradient Approximation in Equality Constrained Deep Declarative Networks
Stephen Gould, Ming Xu, Zhiwei Xu +1
We explore conditions for when the gradient of a deep declarative node can be approximated by ignoring constraint terms and still result in a descent direction for the global loss…
Adversarial Purification with the Manifold Hypothesis
Zhaoyuan Yang, Zhiwei Xu, Jing Zhang +2
In this work, we formulate a novel framework for adversarial robustness using the manifold hypothesis. This framework provides sufficient conditions for defending against adversari…
Exploiting Problem Structure in Deep Declarative Networks: Two Case Studies
Stephen Gould, Dylan Campbell, Itzik Ben-Shabat +2
Deep declarative networks and other recent related works have shown how to differentiate the solution map of a (continuous) parametrized optimization problem, opening up the possib…
A Discrete-event-based Simulator for Distributed Deep Learning
Xiaoyan Liu, Zhiwei Xu, Yana Qin +1
New intelligence applications are driving increasing interest in deploying deep neural networks (DNN) in a distributed way. To set up distributed deep learning involves alterations…
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs
Zhiwei Xu, Thalaiyasingam Ajanthan, Vibhav Vineet +1
Although 3D Convolutional Neural Networks are essential for most learning based applications involving dense 3D data, their applicability is limited due to excessive memory and com…
Refining Semantic Segmentation with Superpixel by Transparent Initialization and Sparse Encoder
Zhiwei Xu, Thalaiyasingam Ajanthan, Richard Hartley
Although deep learning greatly improves the performance of semantic segmentation, its success mainly lies in object central areas without accurate edges. As superpixels are a popul…
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs
Zhiwei Xu, Thalaiyasingam Ajanthan, Vibhav Vineet +1
Although 3D Convolutional Neural Networks (CNNs) are essential for most learning based applications involving dense 3D data, their applicability is limited due to excessive memory…
Fast and Differentiable Message Passing on Pairwise Markov Random Fields
Zhiwei Xu, Thalaiyasingam Ajanthan, Richard Hartley
Despite the availability of many Markov Random Field (MRF) optimization algorithms, their widespread usage is currently limited due to imperfect MRF modelling arising from hand-cra…