1 citations · 1 across the 8 of their papers we have counts for
9 papers
Pion: A Spectrum-Preserving Optimizer via Orthogonal Equivalence Transformation
Kexuan Shi, Hanxuan Li, Zeju Qiu +3
We introduce Pion, a spectrum-preserving optimizer for large language model (LLM) training based on orthogonal equivalence transformation. Unlike additive optimizers such as Adam a…
Orthogonal Model Merging
Sihan Yang, Kexuan Shi, Weiyang Liu
Merging finetuned Large Language Models (LLMs) has become increasingly important for integrating diverse capabilities into a single unified model. However, prevailing model merging…
Model Merging with Functional Dual Anchors
Kexuan Shi, Yandong Wen, Weiyang Liu
Model merging is an efficient post-training strategy for integrating knowledge from multiple finetuned checkpoints of a shared foundation model. Existing methods operate in the par…
Task-Aware Image Signal Processor for Advanced Visual Perception
Kai Chen, Jin Xiao, Leheng Zhang +2
In recent years, there has been a growing trend in computer vision towards exploiting RAW sensor data, which preserves richer information compared to conventional low-bit RGB image…
Learning Pixel-adaptive Multi-layer Perceptrons for Real-time Image Enhancement
Junyu Lou, Xiaorui Zhao, Kexuan Shi +1
Deep learning-based bilateral grid processing has emerged as a promising solution for image enhancement, inherently encoding spatial and intensity information while enabling effici…
Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Model
Leheng Zhang, Weiyi You, Kexuan Shi +1
Diffusion-based image super-resolution methods have demonstrated significant advantages over GAN-based approaches, particularly in terms of perceptual quality. Building upon a leng…
Consistency Trajectory Matching for One-Step Generative Super-Resolution
Weiyi You, Mingyang Zhang, Leheng Zhang +3
Current diffusion-based super-resolution (SR) approaches achieve commendable performance at the cost of high inference overhead. Therefore, distillation techniques are utilized to…
Inductive Gradient Adjustment For Spectral Bias In Implicit Neural Representations
Kexuan Shi, Hai Chen, Leheng Zhang +1
Implicit Neural Representations (INRs), as a versatile representation paradigm, have achieved success in various computer vision tasks. Due to the spectral bias of the vanilla mult…
Improved Implicit Neural Representation with Fourier Reparameterized Training
Kexuan Shi, Xingyu Zhou, Shuhang Gu
Implicit Neural Representation (INR) as a mighty representation paradigm has achieved success in various computer vision tasks recently. Due to the low-frequency bias issue of vani…