8 papers
Distilling Drifting Transformers with Representation Autoencoders
Jiawei Zhang, Mengfei Xia, Gen Li +1
Despite the significant training acceleration and promising performance, Representation Autoencoders (RAEs) are mainly criticized for poor distillation effectiveness. In this work,…
Stage-wise Distortion-Perception Traversal in Zero-shot Inverse Problems with Diffusion Models
Jiawei Zhang, Ziyuan Liu, Leon Yan +2
The distortion-perception (D-P) tradeoff is a fundamental phenomenon of Bayesian inverse problems, which characterizes the inherent tension between distortion performance and perce…
ReTrack: Data Unlearning in Diffusion Models through Redirecting the Denoising Trajectory
Qitan Shi, Cheng Jin, Jiawei Zhang +1
Diffusion models excel at generating high-quality, diverse images but suffer from training data memorization, raising critical privacy and safety concerns. Data unlearning has emer…
MCD: A Unified MultiModal Framework for Optical-SAR Change Detection with Mixture of Experts and Self-Distillation
Ziyuan Liu, Jiawei Zhang, Wenyu Wang +1
Most existing change detection (CD) methods focus on optical images captured at different times, and deep learning (DL) has achieved remarkable success in this domain. However, in…
Improving Diffusion-based Inverse Algorithms under Few-Step Constraint via Learnable Linear Extrapolation
Jiawei Zhang, Ziyuan Liu, Leon Yan +2
Diffusion-based inverse algorithms have shown remarkable performance across various inverse problems, yet their reliance on numerous denoising steps incurs high computational costs…
See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers
Jiaxin Zhuang, Leon Yan, Zhenwei Zhang +3
Time series anomaly detection (TSAD) is becoming increasingly vital due to the rapid growth of time series data across various sectors. Anomalies in web service data, for example,…
Unleashing the Denoising Capability of Diffusion Prior for Solving Inverse Problems
Jiawei Zhang, Jiaxin Zhuang, Cheng Jin +2
The recent emergence of diffusion models has significantly advanced the precision of learnable priors, presenting innovative avenues for addressing inverse problems. Since inverse…
EPA: Neural Collapse Inspired Robust Out-of-Distribution Detector
Jiawei Zhang, Yufan Chen, Cheng Jin +2
Out-of-distribution (OOD) detection plays a crucial role in ensuring the security of neural networks. Existing works have leveraged the fact that In-distribution (ID) samples form…