2 citations · 5 across the 16 of their papers we have counts for
18 papers
Dissect and Prune: Enhancing Robustness in AI-Generated Image Detection
Dahye Kim, Jaehyun Choi, Hyun Seok Seong +4
While existing AI-generated image detectors report high performance, we identify that this is largely driven by a critical prediction asymmetry: a bias toward the real class that s…
Learning Neural Deformation Representation for 4D Dynamic Shape Generation
Gyojin Han, Jiwan Hur, Jaehyun Choi +1
Recent developments in 3D shape representation opened new possibilities for generating detailed 3D shapes. Despite these advances, there are few studies dealing with the generation…
Beyond Semantics: Disentangling Information Scope in Sparse Autoencoders for CLIP
Yusung Ro, Jaehyun Choi, Junmo Kim
Sparse Autoencoders (SAEs) have emerged as a powerful tool for interpreting the internal representations of CLIP vision encoders, yet existing analyses largely focus on the semanti…
Frequency-Aware Token Reduction for Efficient Vision Transformer
Dong-Jae Lee, Jiwan Hur, Jaehyun Choi +2
Vision Transformers have demonstrated exceptional performance across various computer vision tasks, yet their quadratic computational complexity concerning token length remains a s…
DAM: Domain-Aware Module for Multi-Domain Dataset Condensation
Jaehyun Choi, Gyojin Han, Dong-Jae Lee +2
Dataset Condensation (DC) has emerged as a promising solution to mitigate the computational and storage burdens associated with training deep learning models. However, existing DC…
PRISM: Video Dataset Condensation with Progressive Refinement and Insertion for Sparse Motion
Jaehyun Choi, Jiwan Hur, Gyojin Han +2
Video dataset condensation aims to reduce the immense computational cost of video processing. However, it faces a fundamental challenge regarding the inseparable interdependence be…
Self-supervised Transformation Learning for Equivariant Representations
Jaemyung Yu, Jaehyun Choi, Dong-Jae Lee +2
Unsupervised representation learning has significantly advanced various machine learning tasks. In the computer vision domain, state-of-the-art approaches utilize transformations l…
AH-OCDA: Amplitude-based Curriculum Learning and Hopfield Segmentation Model for Open Compound Domain Adaptation
Jaehyun Choi, Junwon Ko, Dong-Jae Lee +1
Open compound domain adaptation (OCDA) is a practical domain adaptation problem that consists of a source domain, target compound domain, and unseen open domain. In this problem, t…
Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-Guidance
Jiwan Hur, Dong-Jae Lee, Gyojin Han +3
Masked generative models (MGMs) have shown impressive generative ability while providing an order of magnitude efficient sampling steps compared to continuous diffusion models. How…
Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency
Woonghyun Ka, Jae Young Lee, Jaehyun Choi +1
In stereo-matching knowledge distillation methods of the self-supervised monocular depth estimation, the stereo-matching network's knowledge is distilled into a monocular depth net…
Modeling Stereo-Confidence Out of the End-to-End Stereo-Matching Network via Disparity Plane Sweep
Jae Young Lee, Woonghyun Ka, Jaehyun Choi +1
We propose a novel stereo-confidence that can be measured externally to various stereo-matching networks, offering an alternative input modality choice of the cost volume for learn…
Expanding Expressiveness of Diffusion Models with Limited Data via Self-Distillation based Fine-Tuning
Jiwan Hur, Jaehyun Choi, Gyojin Han +2
Training diffusion models on limited datasets poses challenges in terms of limited generation capacity and expressiveness, leading to unsatisfactory results in various downstream t…
Deep Cross-Modal Steganography Using Neural Representations
Gyojin Han, Dong-Jae Lee, Jiwan Hur +2
Steganography is the process of embedding secret data into another message or data, in such a way that it is not easily noticeable. With the advancement of deep learning, Deep Neur…
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…
Fix the Noise: Disentangling Source Feature for Controllable Domain Translation
Dongyeun Lee, Jae Young Lee, Doyeon Kim +3
Recent studies show strong generative performance in domain translation especially by using transfer learning techniques on the unconditional generator. However, the control betwee…
I See-Through You: A Framework for Removing Foreground Occlusion in Both Sparse and Dense Light Field Images
Jiwan Hur, Jae Young Lee, Jaehyun Choi +1
Light field (LF) camera captures rich information from a scene. Using the information, the LF de-occlusion (LF-DeOcc) task aims to reconstruct the occlusion-free center view image.…
Data Poisoning Attack Aiming the Vulnerability of Continual Learning
Gyojin Han, Jaehyun Choi, Hyeong Gwon Hong +1
Generally, regularization-based continual learning models limit access to the previous task data to imitate the real-world constraints related to memory and privacy. However, this…
Fix the Noise: Disentangling Source Feature for Transfer Learning of StyleGAN
Dongyeun Lee, Jae Young Lee, Doyeon Kim +2
Transfer learning of StyleGAN has recently shown great potential to solve diverse tasks, especially in domain translation. Previous methods utilized a source model by swapping or f…