5 papers
PRISP: Privacy-Safe Few-Shot Personalization via Lightweight Adaptation
Junho Park, Dohoon Kim, Taesup Moon
Large language model (LLM) personalization aims to adapt general-purpose models to individual users. Most existing methods, however, are developed under data-rich and resource-abun…
DoMIX: An Efficient Framework for Exploiting Domain Knowledge in Fine-Tuning
Dohoon Kim, Donghun Kang, Taesup Moon
Domain-Adaptive Pre-training (DAP) has recently gained attention for its effectiveness in fine-tuning pre-trained models. Building on this, continual DAP has been explored to devel…
An Efficient Post-hoc Framework for Reducing Task Discrepancy of Text Encoders for Composed Image Retrieval
Jaeseok Byun, Seokhyeon Jeong, Wonjae Kim +2
Composed Image Retrieval (CIR) aims to retrieve a target image based on a reference image and conditioning text, enabling controllable image searches. The mainstream Zero-Shot (ZS)…
DEAL: Decoupled Classifier with Adaptive Linear Modulation for Group Robust Early Diagnosis of MCI to AD Conversion
Donggyu Lee, Juhyeon Park, Taesup Moon
While deep learning-based Alzheimer's disease (AD) diagnosis has recently made significant advancements, particularly in predicting the conversion of mild cognitive impairment (MCI…
TLDR: Text Based Last-layer Retraining for Debiasing Image Classifiers
Juhyeon Park, Seokhyeon Jeong, Taesup Moon
An image classifier may depend on incidental features stemming from a strong correlation between the feature and the classification target in the training dataset. Recently, Last L…