47 citations · 54 across the 2 of their papers we have counts for
4 papers · 1 filter
Position-based Scaled Gradient for Model Quantization and Pruning
Jangho Kim, KiYoon Yoo, Nojun Kwak
We propose the position-based scaled gradient (PSG) that scales the gradient depending on the position of a weight vector to make it more compression-friendly. First, we theoretica…
QKD: Quantization-aware Knowledge Distillation
Jangho Kim, Yash Bhalgat, Jinwon Lee +2
Quantization and Knowledge distillation (KD) methods are widely used to reduce memory and power consumption of deep neural networks (DNNs), especially for resource-constrained edge…
Feature Fusion for Online Mutual Knowledge Distillation
Jangho Kim, Minsung Hyun, Inseop Chung +1
We propose a learning framework named Feature Fusion Learning (FFL) that efficiently trains a powerful classifier through a fusion module which combines the feature maps generated…
Paraphrasing Complex Network: Network Compression via Factor Transfer
Jangho Kim, SeongUk Park, Nojun Kwak
Many researchers have sought ways of model compression to reduce the size of a deep neural network (DNN) with minimal performance degradation in order to use DNNs in embedded syste…