167 citations · 736 across the 30 of their papers we have counts for
6 papers · 1 filter
Rethinking Positional Encoding
Jianqiao Zheng, Sameera Ramasinghe, Simon Lucey
It is well noted that coordinate based MLPs benefit -- in terms of preserving high-frequency information -- through the encoding of coordinate positions as an array of Fourier feat…
Architectural Adversarial Robustness: The Case for Deep Pursuit
George Cazenavette, Calvin Murdock, Simon Lucey
Despite their unmatched performance, deep neural networks remain susceptible to targeted attacks by nearly imperceptible levels of adversarial noise. While the underlying cause of…
Dataless Model Selection with the Deep Frame Potential
Calvin Murdock, Simon Lucey
Choosing a deep neural network architecture is a fundamental problem in applications that require balancing performance and parameter efficiency. Standard approaches rely on ad-hoc…
Deep Component Analysis via Alternating Direction Neural Networks
Calvin Murdock, Ming-Fang Chang, Simon Lucey
Despite a lack of theoretical understanding, deep neural networks have achieved unparalleled performance in a wide range of applications. On the other hand, shallow representation…
CNNs are Globally Optimal Given Multi-Layer Support
Chen Huang, Chen Kong, Simon Lucey
Stochastic Gradient Descent (SGD) is the central workhorse for training modern CNNs. Although giving impressive empirical performance it can be slow to converge. In this paper we e…
Take it in your stride: Do we need striding in CNNs?
Chen Kong, Simon Lucey
Since their inception, CNNs have utilized some type of striding operator to reduce the overlap of receptive fields and spatial dimensions. Although having clear heuristic motivatio…