59 citations · 83 across the 10 of their papers we have counts for
10 papers
OmniDrop: Layer-wise Token Pruning for Omni-modal LLMs via Query-Guidance
Yeo Jeong Park, Hyemi Jang, Minseo Choi +3
Omni-modal large language models have demonstrated remarkable potential in holistic multimodal understanding; however, the token explosion caused by high-resolution audio and video…
LookaheadKV: Fast and Accurate KV Cache Eviction by Glimpsing into the Future without Generation
Jinwoo Ahn, Ingyu Seong, Akhil Kedia +4
Transformer-based large language models (LLMs) rely on key-value (KV) caching to avoid redundant computation during autoregressive inference. While this mechanism greatly improves…
On the Importance of a Multi-Scale Calibration for Quantization
Seungwoo Son, Ingyu Seong, Junhan Kim +2
Post-training quantization (PTQ) is a cornerstone for efficiently deploying large language models (LLMs), where a small calibration set critically affects quantization performance.…
Unsupervised Homography Estimation on Multimodal Image Pair via Alternating Optimization
Sanghyeob Song, Jaihyun Lew, Hyemi Jang +1
Estimating the homography between two images is crucial for mid- or high-level vision tasks, such as image stitching and fusion. However, using supervised learning methods is often…
DAFA: Distance-Aware Fair Adversarial Training
Hyungyu Lee, Saehyung Lee, Hyemi Jang +3
The disparity in accuracy between classes in standard training is amplified during adversarial training, a phenomenon termed the robust fairness problem. Existing methodologies aim…
PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image Denoising
Hyemi Jang, Junsung Park, Dahuin Jung +3
Although supervised image denoising networks have shown remarkable performance on synthesized noisy images, they often fail in practice due to the difference between real and synth…
New Insights for the Stability-Plasticity Dilemma in Online Continual Learning
Dahuin Jung, Dongjin Lee, Sunwon Hong +3
The aim of continual learning is to learn new tasks continuously (i.e., plasticity) without forgetting previously learned knowledge from old tasks (i.e., stability). In the scenari…
Stein Latent Optimization for Generative Adversarial Networks
Uiwon Hwang, Heeseung Kim, Dahuin Jung +3
Generative adversarial networks (GANs) with clustered latent spaces can perform conditional generation in a completely unsupervised manner. In the real world, the salient attribute…
PuVAE: A Variational Autoencoder to Purify Adversarial Examples
Uiwon Hwang, Jaewoo Park, Hyemi Jang +2
Deep neural networks are widely used and exhibit excellent performance in many areas. However, they are vulnerable to adversarial attacks that compromise the network at the inferen…
Security and Privacy Issues in Deep Learning
Ho Bae, Jaehee Jang, Dahuin Jung +4
To promote secure and private artificial intelligence (SPAI), we review studies on the model security and data privacy of DNNs. Model security allows system to behave as intended w…