activity
20202022
most citedIs Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study

26 citations · 64 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20221 cited

AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training

Yifan Jiang, Peter Hedman, Ben Mildenhall +4

Neural Radiance Fields (NeRFs) are a powerful representation for modeling a 3D scene as a continuous function. Though NeRF is able to render complex 3D scenes with view-dependent e…

cs.CV202214 cited

NeRF-SOS: Any-View Self-supervised Object Segmentation on Complex Scenes

Zhiwen Fan, Peihao Wang, Yifan Jiang +3

Neural volumetric representations have shown the potential that Multi-layer Perceptrons (MLPs) can be optimized with multi-view calibrated images to represent scene geometry and ap…

cs.LG202126 cited

Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study

Zhiqiang Shen, Zechun Liu, Dejia Xu +3

This work aims to empirically clarify a recently discovered perspective that label smoothing is incompatible with knowledge distillation. We begin by introducing the motivation beh…

cs.CV202020 cited

AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results

Pengxu Wei, Hannan Lu, Radu Timofte +68

This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This ch…

cs.CV20203 cited

NTIRE 2020 Challenge on Image Demoireing: Methods and Results

Shanxin Yuan, Radu Timofte, Ales Leonardis +43

This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020. Demo…