no code implementations • 28 Dec 2023 • Jason Chun Lok Li, Chang Liu, Binxiao Huang, Ngai Wong
Existing approaches to Implicit Neural Representation (INR) can be interpreted as a global scene representation via a linear combination of Fourier bases of different frequencies.
no code implementations • 15 Dec 2023 • Jason Chun Lok Li, Rui Lin, Jiajun Zhou, Edmund Yin Mun Lam, Ngai Wong
Despite the decomposition of convolutional kernels for lightweight CNNs being well studied, existing works that rely on tensor network diagrams or hyperdimensional abstraction lack geometry intuition.
no code implementations • 11 Dec 2023 • Binxiao Huang, Jason Chun Lok Li, Jie Ran, Boyu Li, Jiajun Zhou, Dahai Yu, Ngai Wong
Conventional super-resolution (SR) schemes make heavy use of convolutional neural networks (CNNs), which involve intensive multiply-accumulate (MAC) operations, and require specialized hardware such as graphics processing units.
no code implementations • 14 Nov 2023 • Rui Lin, Jason Chun Lok Li, Jiajun Zhou, Binxiao Huang, Jie Ran, Ngai Wong
Most deep neural networks (DNNs) consist fundamentally of convolutional and/or fully connected layers, wherein the linear transform can be cast as the product between a filter matrix and a data matrix obtained by arranging feature tensors into columns.
no code implementations • 13 Aug 2022 • Jie Ran, Rui Lin, Jason Chun Lok Li, Jiajun Zhou, Ngai Wong
A novel deep neural network (DNN) architecture is proposed wherein the filtering and linear transform are realized solely with product quantization (PQ).