9 papers
FOCUS: Frequency-Optimized Conditioning of DiffUSion Models for mitigating catastrophic forgetting during Test-Time Adaptation
Gabriel Tjio, Jie Zhang, Xulei Yang +6
Test-time adaptation enables models to adapt to evolving domains. However, balancing the tradeoff between preserving knowledge and adapting to domain shifts remains challenging for…
Time-variant Image Inpainting via Interactive Distribution Transition Estimation
Yun Xing, Qing Guo, Xiaoguang Li +5
In this work, we focus on a novel and practical task, i.e., Time-vAriant iMage inPainting (TAMP). The aim of TAMP is to restore a damaged target image by leveraging the complementa…
Trust-Aware Diversion for Data-Effective Distillation
Zhuojie Wu, Yanbin Liu, Xin Shen +2
Dataset distillation compresses a large dataset into a small synthetic subset that retains essential information. Existing methods assume that all samples are perfectly labeled, li…
EmoAttack: Emotion-to-Image Diffusion Models for Emotional Backdoor Generation
Tianyu Wei, Shanmin Pang, Qi Guo +3
Text-to-image diffusion models can generate realistic images based on textual inputs, enabling users to convey their opinions visually through language. Meanwhile, within language,…
Deep Hierarchical Graph Alignment Kernels
Shuhao Tang, Hao Tian, Xiaofeng Cao +1
Typical R-convolution graph kernels invoke the kernel functions that decompose graphs into non-isomorphic substructures and compare them. However, overlooking implicit similarities…
Transductive Reward Inference on Graph
Bohao Qu, Xiaofeng Cao, Qing Guo +3
In this study, we present a transductive inference approach on that reward information propagation graph, which enables the effective estimation of rewards for unlabelled data in o…
Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss
Zhenlong Liu, Lei Feng, Huiping Zhuang +2
Machine learning models are susceptible to membership inference attacks (MIAs), which aim to infer whether a sample is in the training set. Existing work utilizes gradient ascent t…
Nonparametric Teaching for Multiple Learners
Chen Zhang, Xiaofeng Cao, Weiyang Liu +2
We study the problem of teaching multiple learners simultaneously in the nonparametric iterative teaching setting, where the teacher iteratively provides examples to the learner fo…
IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks
Yue Cao, Tianlin Li, Xiaofeng Cao +3
We introduce a novel approach to counter adversarial attacks, namely, image resampling. Image resampling transforms a discrete image into a new one, simulating the process of scene…