30 citations · 91 across the 44 of their papers we have counts for
5 papers · 1 filter
How You Start Matters for Generalization
Sameera Ramasinghe, Lachlan MacDonald, Moshiur Farazi +2
Characterizing the remarkable generalization properties of over-parameterized neural networks remains an open problem. In this paper, we promote a shift of focus towards initializa…
Few-shot Class-incremental Learning for 3D Point Cloud Objects
Townim Chowdhury, Ali Cheraghian, Sameera Ramasinghe +3
Few-shot class-incremental learning (FSCIL) aims to incrementally fine-tune a model (trained on base classes) for a novel set of classes using a few examples without forgetting the…
Trading Positional Complexity vs. Deepness in Coordinate Networks
Jianqiao Zheng, Sameera Ramasinghe, Xueqian Li +1
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…
GARF: Gaussian Activated Radiance Fields for High Fidelity Reconstruction and Pose Estimation
Shin-Fang Chng, Sameera Ramasinghe, Jamie Sherrah +1
Despite Neural Radiance Fields (NeRF) showing compelling results in photorealistic novel views synthesis of real-world scenes, most existing approaches require accurate prior camer…
On Regularizing Coordinate-MLPs
Sameera Ramasinghe, Lachlan MacDonald, Simon Lucey
We show that typical implicit regularization assumptions for deep neural networks (for regression) do not hold for coordinate-MLPs, a family of MLPs that are now ubiquitous in comp…