6 citations · 8 across the 2 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…
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…