3 citations · 10 across the 7 of their papers we have counts for
7 papers
Neural Spectral Decomposition for Dataset Distillation
Shaolei Yang, Shen Cheng, Mingbo Hong +3
In this paper, we propose Neural Spectrum Decomposition, a generic decomposition framework for dataset distillation. Unlike previous methods, we consider the entire dataset as a hi…
MEFLUT: Unsupervised 1D Lookup Tables for Multi-exposure Image Fusion
Ting Jiang, Chuan Wang, Xinpeng Li +3
In this paper, we introduce a new approach for high-quality multi-exposure image fusion (MEF). We show that the fusion weights of an exposure can be encoded into a 1D lookup table…
DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution
Lei Yu, Xinpeng Li, Youwei Li +4
Efficient deep learning-based approaches have achieved remarkable performance in single image super-resolution. However, recent studies on efficient super-resolution have mainly fo…
Fast Nearest Convolution for Real-Time Efficient Image Super-Resolution
Ziwei Luo, Youwei Li, Lei Yu +4
Deep learning-based single image super-resolution (SISR) approaches have drawn much attention and achieved remarkable success on modern advanced GPUs. However, most state-of-the-ar…
Efficient One Pass Self-distillation with Zipf's Label Smoothing
Jiajun Liang, Linze Li, Zhaodong Bing +4
Self-distillation exploits non-uniform soft supervision from itself during training and improves performance without any runtime cost. However, the overhead during training is ofte…
RealFlow: EM-based Realistic Optical Flow Dataset Generation from Videos
Yunhui Han, Kunming Luo, Ao Luo +4
Obtaining the ground truth labels from a video is challenging since the manual annotation of pixel-wise flow labels is prohibitively expensive and laborious. Besides, existing appr…