13 citations · 38 across the 11 of their papers we have counts for
3 papers · 1 filter
Batch Normalization Tells You Which Filter is Important
Junghun Oh, Heewon Kim, Sungyong Baik +2
The goal of filter pruning is to search for unimportant filters to remove in order to make convolutional neural networks (CNNs) efficient without sacrificing the performance in the…
Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning
Sungyong Baik, Janghoon Choi, Heewon Kim +3
In few-shot learning scenarios, the challenge is to generalize and perform well on new unseen examples when only very few labeled examples are available for each task. Model-agnost…
Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report
Andrey Ignatov, Andres Romero, Heewon Kim +28
Video super-resolution has recently become one of the most important mobile-related problems due to the rise of video communication and streaming services. While many solutions hav…