6 papers
AdaRank: Adaptive Rank Pruning for Enhanced Model Merging
Chanhyuk Lee, Jiho Choi, Chanryeol Lee +2
Model merging has emerged as a promising approach for unifying independently fine-tuned models into an integrated framework, significantly enhancing computational efficiency in mul…
Universal Few-Shot Spatial Control for Diffusion Models
Kiet T. Nguyen, Chanhyuk Lee, Donggyun Kim +2
Spatial conditioning in pretrained text-to-image diffusion models has significantly improved fine-grained control over the structure of generated images. However, existing control…
HyperFlow: Gradient-Free Emulation of Few-Shot Fine-Tuning
Donggyun Kim, Chanwoo Kim, Seunghoon Hong
While test-time fine-tuning is beneficial in few-shot learning, the need for multiple backpropagation steps can be prohibitively expensive in real-time or low-resource scenarios. T…
Revisiting Weight Averaging for Model Merging
Jiho Choi, Donggyun Kim, Chanhyuk Lee +1
Model merging aims to build a multi-task learner by combining the parameters of individually fine-tuned models without additional training. While a straightforward approach is to a…
Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the Wild
Donggyun Kim, Seongwoong Cho, Semin Kim +2
Large language models have evolved data-efficient generalists, benefiting from the universal language interface and large-scale pre-training. However, constructing a data-efficient…
Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous Control
Seongwoong Cho, Donggyun Kim, Jinwoo Lee +1
Generalizing across robot embodiments and tasks is crucial for adaptive robotic systems. Modular policy learning approaches adapt to new embodiments but are limited to specific tas…