1 citations · 1 across the 4 of their papers we have counts for
6 papers
SPICE: Simple Polysemantic Feature Interpretation via Clustering-based Explanation
Sehyun Lee, Dahee Kwon, Damin Lee +1
One of the pivotal recent challenges in neural network interpretability is polysemanticity, where a single neuron is activated by multiple, often unrelated concepts, hindering clea…
Breaking the Lock-in: Diversifying Text-to-Image Generation via Representation Modulation
Dahee Kwon, Haeun Lee, Jaesik Choi
Recent text-to-image models built on large-scale Transformer backbones and flow-based objectives deliver strong text-image alignment and high visual quality, yet often produce over…
Granular Concept Circuits: Toward a Fine-Grained Circuit Discovery for Concept Representations
Dahee Kwon, Sehyun Lee, Jaesik Choi
Deep vision models have achieved remarkable classification performance by leveraging a hierarchical architecture in which human-interpretable concepts emerge through the compositio…
Enhancing Creative Generation on Stable Diffusion-based Models
Jiyeon Han, Dahee Kwon, Gayoung Lee +2
Recent text-to-image generative models, particularly Stable Diffusion and its distilled variants, have achieved impressive fidelity and strong text-image alignment. However, their…
Understanding Distributed Representations of Concepts in Deep Neural Networks without Supervision
Wonjoon Chang, Dahee Kwon, Jaesik Choi
Understanding intermediate representations of the concepts learned by deep learning classifiers is indispensable for interpreting general model behaviors. Existing approaches to re…
Edge-oriented Implicit Neural Representation with Channel Tuning
Wonjoon Chang, Dahee Kwon, Bumjin Park
Implicit neural representation, which expresses an image as a continuous function rather than a discrete grid form, is widely used for image processing. Despite its outperforming r…