19 citations · 25 across the 4 of their papers we have counts for
5 papers
Low-Cost Self-Ensembles Based on Multi-Branch Transformation and Grouped Convolution
Hojung Lee, Jong-Seok Lee
Recent advancements in low-cost ensemble learning have demonstrated improved efficiency for image classification. However, the existing low-cost ensemble methods show relatively lo…
Network Fission Ensembles for Low-Cost Self-Ensembles
Hojung Lee, Jong-Seok Lee
Recent ensemble learning methods for image classification have been shown to improve classification accuracy with low extra cost. However, they still require multiple trained model…
Students are the Best Teacher: Exit-Ensemble Distillation with Multi-Exits
Hojung Lee, Jong-Seok Lee
This paper proposes a novel knowledge distillation-based learning method to improve the classification performance of convolutional neural networks (CNNs) without a pre-trained tea…
Local Critic Training for Model-Parallel Learning of Deep Neural Networks
Hojung Lee, Cho-Jui Hsieh, Jong-Seok Lee
In this paper, we propose a novel model-parallel learning method, called local critic training, which trains neural networks using additional modules called local critic networks.…
Local Critic Training of Deep Neural Networks
Hojung Lee, Jong-seok Lee
This paper proposes a novel approach to train deep neural networks by unlocking the layer-wise dependency of backpropagation training. The approach employs additional modules calle…