6 citations · 15 across the 10 of their papers we have counts for
11 papers
StablePrompt: Automatic Prompt Tuning using Reinforcement Learning for Large Language Models
Minchan Kwon, Gaeun Kim, Jongsuk Kim +2
Finding appropriate prompts for the specific task has become an important issue as the usage of Large Language Models (LLM) has expanded. Reinforcement Learning (RL) is widely used…
Beta Sampling is All You Need: Efficient Image Generation Strategy for Diffusion Models using Stepwise Spectral Analysis
Haeil Lee, Hansang Lee, Seoyeon Gye +1
Generative diffusion models have emerged as a powerful tool for high-quality image synthesis, yet their iterative nature demands significant computational resources. This paper pro…
GenMix: Combining Generative and Mixture Data Augmentation for Medical Image Classification
Hansang Lee, Haeil Lee, Helen Hong
In this paper, we propose a novel data augmentation technique called GenMix, which combines generative and mixture approaches to leverage the strengths of both methods. While gener…
Inspecting Explainability of Transformer Models with Additional Statistical Information
Hoang C. Nguyen, Haeil Lee, Junmo Kim
Transformer becomes more popular in the vision domain in recent years so there is a need for finding an effective way to interpret the Transformer model by visualizing it. In recen…
The Effects of Mixed Sample Data Augmentation are Class Dependent
Haeil Lee, Hansang Lee, Junmo Kim
Mixed Sample Data Augmentation (MSDA) techniques, such as Mixup, CutMix, and PuzzleMix, have been widely acknowledged for enhancing performance in a variety of tasks. A previous st…
Reinforcement Learning-Based Black-Box Model Inversion Attacks
Gyojin Han, Jaehyun Choi, Haeil Lee +1
Model inversion attacks are a type of privacy attack that reconstructs private data used to train a machine learning model, solely by accessing the model. Recently, white-box model…
Noisy Label Classification using Label Noise Selection with Test-Time Augmentation Cross-Entropy and NoiseMix Learning
Hansang Lee, Haeil Lee, Helen Hong +1
As the size of the dataset used in deep learning tasks increases, the noisy label problem, which is a task of making deep learning robust to the incorrectly labeled data, has becom…
Test-Time Mixup Augmentation for Data and Class-Specific Uncertainty Estimation in Deep Learning Image Classification
Hansang Lee, Haeil Lee, Helen Hong +1
Uncertainty estimation of trained deep learning networks is valuable for optimizing learning efficiency and evaluating the reliability of network predictions. In this paper, we pro…
Progressive Seed Generation Auto-encoder for Unsupervised Point Cloud Learning
Juyoung Yang, Pyunghwan Ahn, Doyeon Kim +2
With the development of 3D scanning technologies, 3D vision tasks have become a popular research area. Owing to the large amount of data acquired by sensors, unsupervised learning…
PBP-Net: Point Projection and Back-Projection Network for 3D Point Cloud Segmentation
JuYoung Yang, Chanho Lee, Pyunghwan Ahn +3
Following considerable development in 3D scanning technologies, many studies have recently been proposed with various approaches for 3D vision tasks, including some methods that ut…
Do Vision Models Encode Object-Level Semantic Relatedness? A Cognitive Psychology-Inspired Benchmark
Hansang Lee, Haeil Lee, Junmo Kim
Modern vision models have achieved strong object-recognition performance, yet it remains unclear whether their representations encode object-level semantic relatedness, the meaning…