47 citations · 54 across the 2 of their papers we have counts for
7 papers
Detection of Word Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density Estimation
KiYoon Yoo, Jangho Kim, Jiho Jang +1
Word-level adversarial attacks have shown success in NLP models, drastically decreasing the performance of transformer-based models in recent years. As a countermeasure, adversaria…
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…
Feature-map-level Online Adversarial Knowledge Distillation
Inseop Chung, SeongUk Park, Jangho Kim +1
Feature maps contain rich information about image intensity and spatial correlation. However, previous online knowledge distillation methods only utilize the class probabilities. T…
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…
StackNet: Stacking Parameters for Continual learning
Jangho Kim, Jeesoo Kim, Nojun Kwak
Training a neural network for a classification task typically assumes that the data to train are given from the beginning. However, in the real world, additional data accumulate gr…