71 citations · 152 across the 9 of their papers we have counts for
9 papers
Magnitude Attention-based Dynamic Pruning
Jihye Back, Namhyuk Ahn, Jangho Kim
Existing pruning methods utilize the importance of each weight based on specified criteria only when searching for a sparse structure but do not utilize it during training. In this…
Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization
Byeonggeun Kim, Seunghan Yang, Jangho Kim +1
It is a practical research topic how to deal with multi-device audio inputs by a single acoustic scene classification system with efficient design. In this work, we propose Residua…
Self-Distilled Self-Supervised Representation Learning
Jiho Jang, Seonhoon Kim, Kiyoon Yoo +3
State-of-the-art frameworks in self-supervised learning have recently shown that fully utilizing transformer-based models can lead to performance boost compared to conventional CNN…
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble
Gaon An, Seungyong Moon, Jang-Hyun Kim +1
Offline reinforcement learning (offline RL), which aims to find an optimal policy from a previously collected static dataset, bears algorithmic difficulties due to function approxi…
Dynamic Collective Intelligence Learning: Finding Efficient Sparse Model via Refined Gradients for Pruned Weights
Jangho Kim, Jayeon Yoo, Yeji Song +2
With the growth of deep neural networks (DNN), the number of DNN parameters has drastically increased. This makes DNN models hard to be deployed on resource-limited embedded system…
PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation
Jangho Kim, Simyung Chang, Nojun Kwak
As edge devices become prevalent, deploying Deep Neural Networks (DNN) on edge devices has become a critical issue. However, DNN requires a high computational resource which is rar…
Prototype-based Personalized Pruning
Jangho Kim, Simyung Chang, Sungrack Yun +1
Nowadays, as edge devices such as smartphones become prevalent, there are increasing demands for personalized services. However, traditional personalization methods are not suitabl…
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity
Jang-Hyun Kim, Wonho Choo, Hosan Jeong +1
While deep neural networks show great performance on fitting to the training distribution, improving the networks' generalization performance to the test distribution and robustnes…
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
Jang-Hyun Kim, Wonho Choo, Hyun Oh Song
While deep neural networks achieve great performance on fitting the training distribution, the learned networks are prone to overfitting and are susceptible to adversarial attacks.…